Latent load calibration for calorimetry using sensor fusion

ABSTRACT

In one aspect, the present disclosure relates to a method including obtaining, by a heart rate sensor of a fitness tracking device, a heart rate measurement of a user of the fitness tracking device; obtaining, by at least one motion sensor, motion data of the user; analyzing, by the fitness tracking device, the motion data of the user to estimate a step rate of the user; estimating, by the fitness tracking device, a load associated with a physical activity of the user by comparing the heart rate measurement with the step rate of the user; and estimating, by the fitness tracking device, an energy expenditure rate of the user using the load and at least one of the heart rate measurement and the step rate.

PRIORITY

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/044,846, titled “Calibration and Calorimetry Using Motion andHeart Rate Sensors,” filed on Sep. 2, 2014, which is hereby incorporatedby reference in its entirety.

CROSS-REFERENCE TO RELATED APPLICATIONS

This disclosure is related to U.S. patent application Ser. No.14/501,634, titled “Method and System to Calibrate Fitness Level andDirect Calorie Burn Using Motion, Location Sensing, and Heart Rate,”filed Sep. 30, 2014; U.S. patent application Ser. No. 14/501,771, titled“Accurate calorimetry for Intermittent Exercises,” filed Sep. 30, 2014;U.S. patent application Ser. No. 14/501,930, titled “Method and Systemto Estimate Day-Long Calorie Expenditure Based on Posture,” filed Sep.30, 2014; U.S. patent application Ser. No. 14/502,724, titled “SensorFusion Approach to Energy Expenditure Estimation,” filed Sep. 30, 2014;U.S. patent application Ser. No. 14/502,781, titled “Terrain TypeInference from Wearable with Motion Sensing,” filed Sep. 30, 2014; U.S.patent application Ser. No. 14/502,809, titled “Method to EstimatePhysical Activity Rating from Pedometer Data,” filed Sep. 30, 2014; U.S.patent application Ser. No. 14/501,701, titled “Local Model forcalorimetry,” filed Sep. 30, 2014; U.S. Provisional Patent ApplicationNo. 62/044,844 titled “Context-aware Heart Rate Estimation,” filed Sep.2, 2014; U.S. patent application Ser. No. 14/466,890, titled “Heart RatePath Optimizer,” filed on Aug. 22, 2014; and U.S. Provisional PatentApplication No. 62/005,780, titled “Automatic Track Selection forCalibration of Pedometer Devices,” filed on May 30, 2014; which arehereby incorporated by reference in their entirety.

FIELD

The present disclosure relates generally to improving calorieexpenditure prediction and tracking and, more particularly, totechniques for calibration and calorimetry using data from motionssensors and heart rate sensors.

BACKGROUND

An individual's health or fitness can be assessed from the perspectiveof energy expenditure over time. One technique for estimating energyexpenditure, or calorie burn, is based on heart rate. During moderate tovigorous exercise, heart rate relates linearly to energy expenditure.

At a macroscopic level, an individual's heart rate indicates how quicklythe individual's body is delivering oxygen to vital organs and tissues,which consume the oxygen through oxidative cellular metabolism. Theheart pumps blood through the lungs, where blood cells absorb oxygenfrom the lungs. This oxygen-rich blood returns to the heart, from whichit is pumped through blood vessels that distribute the blood throughoutthe body to its organs and tissues. Tissues absorb oxygen carried by theblood and use the oxygen in chemical reactions of oxidative metabolism,also known as aerobic metabolism, to provide energy for biologicalfunctions.

The rate at which an individual body consumes oxygen at a given point intime is referred to as the volumetric flow of oxygen into the tissues ofthe body, also known as “oxygen uptake,” or simply {dot over (V)}O₂(e.g., liters of oxygen per minute). Controlling for differences in bodysize, {dot over (V)}O₂ is often reported for a given individual in termsof oxygen volume at standard temperature and pressure per unit of timeper unit of body mass (e.g., ml/kg/min).

Specifically, {dot over (V)}O₂ measures the overall rate at which thebody is engaged in oxidative metabolism. {dot over (V)}O₂ during variousphysical activities—and, consequently, energy expenditure during thosephysical activities—varies from individual to individual. In alaboratory setting, it may be possible to use indirect calorimetry(e.g., with a {dot over (V)}O₂ mask, heart rate monitors, etc.), tomeasure an individual's aerobic capacity, also known as maximum {dotover (V)}O₂, or simply “{dot over (V)}O₂max.” {dot over (V)}O₂max is themaximum value of {dot over (V)}O₂ (e.g., measured with indirectcalorimetry) that individual can consume.

Another measurement, related to {dot over (V)}O₂, is the MetabolicEquivalent of Task (MET), or simply “metabolic equivalent.”Conventionally, 1 MET was defined to represent an “average” person'sResting Metabolic Rate (RMR) while sitting quietly. 1 MET was set toequal a {dot over (V)}O₂ of 3.5 ml/kg/min, or 1 kcal/kg/hr. Actualresting (or basal) metabolic rates will vary from one individual to thenext. METs provide a way to compare calorie expenditure for differenttasks. For example, if the task of walking slowly is designated as 2METs (2 kcal/kg/hr), it signifies that walking slowly consumes twice asmuch energy as sitting quietly.

When these parameters are known, it may be possible to calibrate afitness tracking device with more accurate calorimetry. Thus, at a givenheart rate during moderate to vigorous aerobic exercise, a device may becapable of calculating a calorie burn rate that is calibrated for theindividual. However, in practice, many individuals will not know their{dot over (V)}O₂max.

SUMMARY

Embodiments of the present disclosure include a fitness tracking deviceand techniques for accurately tracking an individual's energyexpenditure over time and over a variety of activities while wearing thefitness tracking device. In some embodiments, the fitness trackingdevice may be a wearable device. The wearable device may be worn on awrist, such as a watch, and it may include one or more microprocessors,a display, and a variety of sensors, including a heart rate sensor andone or more motion sensors.

Embodiments of the present disclosure may provide accurate,individualized calorimetry throughout a person's day, and across avariety of activities. Some embodiments may calibrate a fitness trackingdevice for an individual without necessarily relying on measuring {dotover (V)}O₂, heart rate testing, or self-reporting about physicalactivity. Additionally, some embodiments may model calorie burn andperform calorimetry processes for a variety of activities accurately,such as walking, running, cycling, or weight lifting. Also, someembodiments may use sensor data to estimate load (e.g., resistance,incline, grade, etc.) for exercise equipment such as ellipticaltrainers, step machines, treadmills, etc., and some embodiments may usesensor data to determine or predict a type of terrain (e.g., pavement,gravel, etc.), to model energy expenditure more accurately by takingthese estimations into account. Furthermore, some embodiments may modelcalorie burn and perform calorimetry processes for sedentary andlow-intensity activity (e.g., when not in an active exercise session),including sitting or standing.

In some embodiments, the heart rate sensor may include aphotoplethysmogram (PPG) sensor for sensing heart rate. The PPG sensorcan illuminate the user's skin using a light, such as a light-emittingdiode (LED), and can measure changes in light absorption as blood ispumped through the subcutaneous tissue under the PPG sensor. The fitnesstracking device can measure an individual's current heart rate from thePPG. The heart rate sensor may also be configured to determine aconfidence level indicating a relative likelihood of an accuracy of agiven heart rate measurement.

In some embodiments, the motion sensors may include, for example, anaccelerometer, a gyroscope, etc. The fitness tracking device may alsoinclude a motion coprocessor, which may be optimized for low-power,continuous motion sensing and processing.

In some embodiments, the fitness tracking device may be capable ofcommunicating with a companion device. The fitness tracking device maycommunicate with a companion device wirelessly, e.g., via a Bluetoothconnection or similar wireless communication method. The companiondevice may be a second mobile device, such as a phone, which may includeadditional sensors. The additional sensors in the companion device mayinclude a Global Positioning System (GPS) sensor, accelerometer,gyroscope, altimeter, motion coprocessor, etc. The companion device may,for example, communicate location information based on data from the GPSsensor to the fitness tracking device.

In some embodiments, a new fitness tracking device may be calibrated tomeasure energy expenditure based on heart rate and motion data. Out ofthe box, the new fitness tracking device may assume a default physicalactivity level (“PAL”) for the user, which in turn may be used toestimate calorie burn for a variety of activities. As the user wears thenew fitness tracking device over time, the new fitness tracking devicemay improve its estimate of the user's PAL. In some embodiments, the newfitness tracking device may consider a rolling average number of dailysteps to adjust the user's PAL. Instead of, or in addition to, relyingon pedometry to adjust the user's PAL, the new fitness tracking devicemay undergo active calibration by, for example, monitoring the user'sheart rate and location information during at least a brief session ofmoderate to vigorous intensity (e.g., a ten-minute run).

In some embodiments, a fitness tracking device may apply particularmodels and algorithms based on prior calibration to compute energyexpenditure given information about the user's heart rate, or motion, ora combination of the two. Energy expenditure computations may also takeinto account information from a companion device, such as GPS locationinformation. The fitness tracking device may apply different models ordifferent algorithms for different types of activity. For example, thefitness tracking device may use motion sensing information to determinewhether a sedentary user is sitting down (burning relatively fewercalories) or standing up (burning relatively more calories).

In some embodiments, the fitness tracking device may apply yet anothermodel for a user while walking, which may be based on pedometry and stepcounting.

In some embodiments, the fitness tracking device may improve theaccuracy of its energy expenditure calculations by accounting foractivity onset and cool down phases that occur, sometimes frequently,during intermittent physical activity such as weight lifting.

In some embodiments, the fitness tracking device may use a combinationof motion and heart rate data to measure calorie burn during active andpaced exercises such as running or cycling. Additionally, the fitnesstracking device may make additional optimizations when a user is cyclingby, for example, applying a work rate model that accounts for additionalforces such as rolling resistance and aerodynamic drag. In someembodiments, the fitness tracking device may adjust the work rate modelfor cycling further by, for example, using motion data to detect thetype of terrain on which the user is cycling.

In some embodiments, the fitness tracking device may use a combinationof motion and heart rate data to estimate the load (or resistance)being, for example, generated by exercise equipment such as anelliptical trainer, step machine, etc.

In some embodiments, the fitness tracking device may use activityclassification based on motion or heart rate data to automaticallydetermine what type of physical activity (e.g., running) that a user isengaged in, and when that activity begins and ends. In otherembodiments, the fitness tracking device may receive input from the userabout when a user begins or ends an activity session, and what type ofactivity (e.g., running or cycling) the user is beginning or ending.

In some embodiments, the fitness tracking device may make furtheroptimizations to compensate for limited system resources, such aslimited battery power or limited memory capacity. For example, thefitness tracking device may decrease the frequency of instantaneousheart rate monitoring when a user is determined to be out of session,and increase the frequency of heart rate monitoring when a user isdetermined to be in session. For another example, the fitness trackingdevice may use local models to maintain highly accurate energyexpenditure estimates while also significantly reducing the amount ofpower or memory needed to perform the calculations based on a localmodel technique as opposed to a nonlocal modeling technique. In someembodiments, the local models may be linear, quadratic, or higher ordernonlinear equations.

In one aspect, the present disclosure relates to a method includingobtaining, by a heart rate sensor of a fitness tracking device, a heartrate measurement of a user of the fitness tracking device; obtaining, byat least one motion sensor, motion data of the user; analyzing, by thefitness tracking device, the motion data of the user to estimate a steprate of the user; estimating, by the fitness tracking device, a loadassociated with a physical activity of the user by comparing the heartrate measurement with the step rate of the user; and estimating, by thefitness tracking device, an energy expenditure rate of the user usingthe load and at least one of the heart rate measurement and the steprate.

In some embodiments, the physical activity of the user can includetraining on an elliptical training machine, and wherein the load caninclude a resistance setting of the elliptical training machine. In someembodiments, the physical activity of the user can include running on atreadmill, and wherein the load can include an incline setting of thetreadmill. In some embodiments, the physical activity of the user caninclude stationary cycling, and wherein the load can include aresistance setting of a stationary cycle. In some embodiments, themethod can include analyzing, by the fitness tracking device, the motiondata to determine a speed of the user, wherein the physical activity ofthe user can include running, and wherein the load can include a gradeof a terrain on which the user can include running. In some embodiments,a first heart rate measurement indicates a first load for a given steprate, and wherein a second heart rate measurement different from thefirst heart rate measurement indicates a second load for the given steprate different from the first load for the given step rate. In someembodiments, a first step rate indicates a first load for a given heartrate, and wherein a second step rate different from the first step rateindicates a second load for the given heart rate different from thefirst load for the given step rate.

In one aspect, the present disclosure relates to a fitness trackingdevice including a heart rate sensor for measuring a heart rate of auser of the fitness tracking device; a motion sensor for sensing motionof the user; and a processor communicatively coupled to the heart ratesensor and the motion sensor, wherein the processor is configured toobtain a heart rate measurement of the user from the heart rate sensor;obtain motion data of the user from the motion sensor; analyze themotion data of the user to estimate a step rate of the user; estimate aload associated with a physical activity of the user by comparing theheart rate measurement with the step rate of the user; and estimate anenergy expenditure rate of the user using the load and at least one ofthe heart rate measurement and the step rate.

In some embodiments, the physical activity of the user can includetraining on an elliptical training machine, and wherein the load caninclude a resistance setting of the elliptical training machine. In someembodiments, the physical activity of the user can include running on atreadmill, and wherein the load can include an incline setting of thetreadmill. In some embodiments, the physical activity of the user caninclude stationary cycling, and the load can include a resistancesetting of a stationary cycle. In some embodiments, the processor isfurther configured to: analyze the motion data to determine a speed ofthe user, wherein the physical activity of the user can include running,and wherein the load can include a grade of a terrain on which the useris running. In some embodiments, a first heart rate measurementindicates a first load for a given step rate, and a second heart ratemeasurement different from the first heart rate measurement indicates asecond load for the given step rate different from the first load forthe given step rate. In some embodiments, a first step rate indicates afirst load for a given heart rate, and a second step rate different fromthe first step rate indicates a second load for the given heart ratedifferent from the first load for the given step rate.

In one aspect, the present disclosure relates to an article ofmanufacture including, a non-transitory processor readable storagemedium; and instructions stored on the medium; wherein the instructionsare configured to be readable from the medium by a processor of afitness tracking device, the fitness tracking device including a heartrate sensor and a motion sensor, and the instructions thereby cause theprocessor to operate to obtain a heart rate measurement of a user of thefitness tracking device from the heart rate sensor; obtain motion dataof the user of the fitness tracking device from the motion sensor;analyze the motion data of the user to estimate a step rate of the user;estimate a load associated with a physical activity of the user bycomparing the heart rate measurement with the step rate of the user; andestimate an energy expenditure rate of the user using the load and atleast one of the heart rate measurement and the step rate.

In some embodiments, the physical activity of the user can includetraining on an elliptical training machine, and the load can include aresistance setting of the elliptical training machine. In someembodiments, the physical activity of the user can include running on atreadmill, and the load can include an incline setting of the treadmill.In some embodiments, the physical activity of the user can includestationary cycling, and the load can include a resistance setting of astationary cycle. In some embodiments, the processor is further causedto: analyze the motion data to determine a speed of the user, whereinthe physical activity of the user can include running, and wherein theload can include a grade of a terrain on which the user is running. Insome embodiments, a first heart rate measurement indicates a first loadfor a given step rate, and a second heart rate measurement differentfrom the first heart rate measurement indicates a second load for thegiven step rate different from the first load for the given step rate.

Other features and advantages will become apparent from the followingdetailed description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to facilitate a fuller understanding of the present disclosure,reference is now made to the accompanying drawings, in which likeelements are referenced with like numerals. These drawings should not beconstrued as limiting the present disclosure, but are intended to beillustrative only.

FIG. 1 shows a fitness tracking device in accordance with an embodimentof the present disclosure.

FIG. 2 depicts a block diagram of a fitness tracking device inaccordance with an embodiment of the present disclosure.

FIG. 3 shows a companion device in accordance with an embodiment of thepresent disclosure.

FIG. 4 shows a calibration method in accordance with an embodiment ofthe present disclosure.

FIG. 5 depicts a block diagram of a calibration technique for inferringphysical activity level in accordance with an embodiment of the presentdisclosure.

FIG. 6 depicts a block diagram of a calibration technique for inferringphysical activity level in accordance with an embodiment of the presentdisclosure.

FIG. 7 depicts a block diagram of a calibration technique for inferringphysical activity level in accordance with an embodiment of the presentdisclosure.

FIGS. 8A-C show chart representations of a calibration technique forinferring physical activity level in accordance with embodiments of thepresent disclosure.

FIG. 9 shows a high-intensity calibration method in accordance with anembodiment of the present disclosure.

FIG. 10 shows a high-intensity calorimetry method in accordance with anembodiment of the present disclosure.

FIG. 11 depicts a terrain detection method in accordance with anembodiment of the present disclosure.

FIG. 12 shows a load estimation method in accordance with an embodimentof the present disclosure.

FIG. 13 shows a load estimation method in accordance with an embodimentof the present disclosure.

FIG. 14 shows a calorimetry method for intermittent activity inaccordance with an embodiment of the present disclosure.

FIG. 15 shows a posture detection method for sedentary calorimetry inaccordance with an embodiment of the present disclosure.

FIG. 16 depicts a posture detection method in accordance with anembodiment of the present disclosure.

FIG. 17 shows a posture detection method for sedentary activity inaccordance with an embodiment of the present disclosure.

FIG. 18 illustrates a posture detection technique in accordance with anembodiment of the present disclosure.

FIGS. 19A-B illustrate posture detection techniques in accordance withembodiments of the present disclosure.

FIG. 20 shows a speed-based calorimetry method in accordance with anembodiment of the present disclosure.

FIG. 21 shows a calorimetry technique using local models in accordancewith an embodiment of the present disclosure.

DESCRIPTION

There is growing interest to assess and monitor one's health or fitnessand physical activity. The present disclosure describes a fitnesstracking device that may be configured to provide an accurate,individualized quantification of energy expenditure over time and acrossa variety of activities. The device may implement sophisticatedcalorimetry techniques based on empirical models and sophisticatedalgorithms that may use motion data, heart rate data, or a weightedcombination of both motion data and heart rate data.

FIG. 1 shows an example of a fitness tracking device 100 in accordancewith an embodiment of the present disclosure. In some embodiments, thefitness tracking device 100 may be a wearable device, such as a watchconfigured to be worn around an individual's wrist. As described in moredetail below, the fitness tracking device 100 may be calibratedaccording to physical attributes of the individual and physical activityby the individual user who is wearing the fitness tracking device 100.

FIG. 2 depicts a block diagram of example components that may be foundwithin the fitness tracking device 100 in accordance with an embodimentof the present disclosure. These components may include a heart ratesensing module 210, a motion sensing module 220, a display module 230,and an interface module 240.

The heart rate sensing module 210 may include or may be in communicationwith a PPG sensor as previously described. The fitness tracking devicecan measure an individual's current heart rate from the PPG. The heartrate sensor may also be configured to determine a confidence levelindicating a relative likelihood of an accuracy of a given heart ratemeasurement. In other embodiments, a traditional heart rate monitor maybe used and may communicate with the fitness tracking device 100 througha near field communication method (e.g., Bluetooth).

The fitness tracking device 100 may include an LED and a photodiode orthe equivalent to obtain a PPG. The fitness tracking device 100 maysubsequently determine the user's current heart rate based on the PPG.

To conserve battery power on the fitness tracking device 100, the LEDmay be a relatively low-power LED, such as a green LED. In someembodiments, to further conserve power on the fitness tracking device100, the fitness tracking device 100 may be configured to check heartrate at periodic intervals (e.g., once per minute, or once per threeminutes). The period for checking heart rate may change dynamically. Forexample, if the fitness tracking device 100 automatically detects orreceives input from the user that the user is engaged in a certainlevel, intensity, or type of physical activity (i.e., “in session”), thefitness tracking device may check heart rate more frequently (e.g., onceper thirty seconds, once per minute, etc.). The fitness tracking device100 may use, for example, machine learning techniques, battery powermonitoring, or physical activity monitoring to balance the frequency ofheart rate samples for accurate calorimetry with power optimization.

In addition to the heart rate sensing module 210, the fitness trackingdevice 100 may also include the motion sensing module 220. The motionsensing module 220 may include one or more motion sensors, such as anaccelerometer or a gyroscope. In some embodiments, the accelerometer maybe a three-axis, microelectromechanical system (MEMS) accelerometer, andthe gyroscope may be a three-axis MEMS gyroscope. A microprocessor (notshown) or motion coprocessor (not shown) of the fitness tracking device100 may receive motion information from the motion sensors of the motionsensing module 220 to track acceleration, rotation, position, ororientation information of the fitness tracking device 100 in sixdegrees of freedom through three-dimensional space. As described in moredetail herein, motion data obtained by the motion sensing module 220 maybe used for a variety of purposes, including work rate modeling,automatic activity classification, pedometry, posture detection, cyclingterrain identification, etc.

In some embodiments, the motion sensing module 220 may include othertypes of sensors in addition to accelerometers and gyroscopes. Forexample, the motion sensing module 220 may include an altimeter, orother types of location sensors, such as a GPS sensor.

In some embodiments, the fitness tracking device 100 may take advantageof the knowledge that the heart rate sensing module 210 and the motionsensing module 220 are approximately collocated in space and time tocombine data from each module 210 and 220 to improve the accuracy of itscalorimetry functionality. Depending on the current activity and adetermination of a confidence of current heart rate and motion data, thefitness tracking device 100 may also rely on one of either the heartrate or a motion-derived work rate to estimate energy expenditure moreaccurately.

The fitness tracking device 100 may also include a display module 230.Display module 230 may be a screen, such as a sapphire or glasstouchscreen, configured to provide output to the user as well as receiveinput form the user via touch. For example, display 230 may beconfigured to show the user a current heart rate or a daily averageenergy expenditure. Display module 230 may receive input form the userto select, for example, which information should be displayed, orwhether the user is beginning a physical activity (i.e., starting asession) or ending a physical activity (i.e., ending a session), such asa running session or a cycling session. In some embodiments, the fitnesstracking device 100 may present output to the user in other ways, suchas by producing sound with a speaker (not shown), and the fitnesstracking device 100 may receive input from the user in other ways, suchas by receiving voice commands via a microphone (not shown).

In some embodiments, the fitness tracking device 100 may communicatewith external devices via interface module 240, including aconfiguration to present output to a user or receive input from a user.Interface module 240 may be a wireless interface. The wireless interfacemay be a standard Bluetooth (IEEE 802.15) interface, such as Bluetoothv4.0, also known as “Bluetooth low energy.” In other embodiments, theinterface may operate according to a cellphone network protocol such asLTE or a Wi-Fi (IEEE 802.11) protocol. In other embodiments, interfacemodule 240 may include wired interfaces, such as a headphone jack or busconnector (e.g., Lightning, Thunderbolt, USB, etc.).

The fitness tracking device 100 may be configured to communicate with acompanion device 300 (FIG. 3), such as a smartphone, as described inmore detail herein. In some embodiments, the fitness tracking device 100may be configured to communicate with other external devices, such as anotebook or desktop computer, tablet, headphones, Bluetooth headset,etc.

The modules described above are examples, and embodiments of the fitnesstracking device 100 may include other modules not shown. For example,the fitness tracking device 100 may include one or more microprocessors(not shown) for processing heart rate data, motion data, otherinformation in the fitness tracking device 100, or executinginstructions for firmware or apps stored in a non-transitoryprocessor-readable medium such as a memory module (not shown).Additionally, some embodiments of the fitness tracking device 100 mayinclude a rechargeable battery (e.g., a lithium-ion battery), amicrophone or a microphone array, one or more cameras, one or morespeakers, a watchband, a sapphire or glass-covered scratch-resistantdisplay, water-resistant casing, etc.

FIG. 3 shows an example of a companion device 300 in accordance with anembodiment of the present disclosure. The fitness tracking device 100may be configured to communicate with the companion device 300, such asby a Bluetooth connection. In some embodiments, the companion device 300may be a smartphone, tablet, or similar portable computing device. Thecompanion device 300 may be carried by the user, stored in the user'spocket, strapped to the user's arm with an armband or similar device,placed on a table, or otherwise positioned within communicable range ofthe fitness tracking device 100.

The companion device 300 may include a variety of sensors, such aslocation and motion sensors (not shown). When the companion device 300may be optionally available for communication with the fitness trackingdevice 100, the fitness tracking device 100 may receive additional datafrom the companion device 300 to improve or supplement its calibrationor calorimetry processes. For example, in some embodiments, the fitnesstracking device 100 may not include a GPS sensor as opposed to analternative embodiment in which the fitness tracking device 100 mayinclude a GPS sensor. In the case where the fitness tracking device 100may not include a GPS sensor, a GPS sensor of the companion device 300may collect GPS location information, and the fitness tracking device100 may receive the GPS location information via interface module 240(FIG. 2) from the companion device 300.

In another example, the fitness tracking device 100 may not include analtimeter, as opposed to an alternative embodiment in which the fitnesstracking device 100 may include an altimeter. In the case where thefitness tracking device 100 may not include an altimeter, an altimeterof the companion device 300 may collect altitude or relative altitudeinformation, and the fitness tracking device 100 may receive thealtitude or relative altitude information via interface module 240 (FIG.2) from the companion device 300.

Calibration—Overview

The following section describes calibration of the fitness trackingdevice 100 according to embodiments of the present disclosure. Threeexamples of methods of calibration described herein include: 1) default,or “out-of-the-box” calibration, 2) calibration by inferring a user'sphysical activity level, and 3) calibration based on “active,”high-intensity heart rate and work rate data.

As explained above, an individual exhibits a linear relationship betweenheart rate (varying between the individual's minimum and maximum heartrates) and {dot over (V)}O₂ (up to the individual's aerobic capacity, or{dot over (V)}O₂max). Additionally, {dot over (V)}O₂ is linked to auser's aerobic power output based on the user's metabolic rate, whichmay also vary from one individual to the next. Metabolic rate may beexpressed in Metabolic Equivalents of Task, or METs. METs indicates howmany calories a “typical” individual burns per unit of body mass perunit of time.

If the user's weight is known, and the user undergoes testing to measurethe user's maximum heart rate and {dot over (V)}O₂max, a device may beable to construct an individualized model of energy expenditure for agiven heart rate.

In situations such as laboratory testing, it may be possible to test andmeasure a user's {dot over (V)}O₂max and maximum heart rate (“HRmax”).With these predetermined values, a device may be able to estimate energyexpenditure more accurately based on a user's current heart rate duringmoderate to high-intensity physical activity or exercise. Withoutlaboratory testing (e.g., testing based on indirect calorimetry), {dotover (V)}O₂max and HRmax may be estimated with other methods, such assubmaximal exercise testing or non-exercise testing. For example, HRmaxmay be estimated based on the user's age. In some embodiments, if aheart rate greater than HRmax is observed, then the device may updatethe estimate of HRmax to use the higher, observed heart rate. In someembodiments, the device may determine whether to use an age-basedestimate or a higher observed heart rate based on a confidence level forthe heart rate measurement or whether the higher observed heart rate wassustained for a threshold period of time.

The user's minimum heart rate (HRmin or HR₀) and basal metabolic rate(e.g., MR₀) may be observed by the device as well or adjusted if evenlower measurements are observed.

Calibration—Default Physical Activity Level (PAL)

Although a fitness tracking device 100 may be configured to calibrateitself based on heart rate measurements and other observations, when auser first takes a fitness tracking device 100 out of its box and wearsit for the first time, the fitness tracking device 100 may not know theuser's {dot over (V)}O₂max or other physical characteristics that it mayneed to measure energy expenditure more accurately.

FIG. 4 shows a calibration method 400 in accordance with an embodimentof the present disclosure. When a user wears, turns on, or otherwiseuses a new fitness tracking device 100 for the first time, the fitnesstracking device 100 may not have a record of any information about thephysical characteristics or physical activity level (“PAL”) of the user,or other information about the user such as heart rate data or work ratedata.

At block 410, the user's physical characteristics may be obtained by thefitness tracking device 100. The obtained physical characteristics mayinclude the user's age (e.g., based on the user's birth date, or basedon a selected age, or based on a selected age range, etc.). The obtainedphysical characteristic's may also include the user's weight or weightrange in, for example, pounds or kilograms. In some embodiments, thefitness tracking device 100 may automatically select or convert unitsaccording to the user's locale. The obtained physical characteristicsmay also include the user's sex (e.g., male or female).

In some embodiments, the fitness tracking device 100 may obtain thisinformation automatically via interface module 240 (FIG. 2) from acompanion device 300 (FIG. 3) or another device. In other embodiments,the fitness tracking device 100 may obtain this information from theuser's account on a cloud-based data storage service after receiving theuser's credentials to access the user's account. In other embodiments,the fitness tracking device 100 may obtain this information by promptingthe user to provide it, such as via a touch-based interface or avoice-based interface.

The fitness tracking device 100 may be configured to store the user'sphysical characteristics in a memory module (e.g., a portion of anon-volatile NAND flash memory chip). In some embodiments, the fitnesstracking device 100 may be configured to update a record of the user'sphysical characteristics at appropriate intervals. For example, thefitness tracking device 100 may be configured to increment the user'sage annually on the user's birthday, or on the anniversary of the user'sinitial age selection, or at other suitable intervals. In someembodiments, the fitness tracking device 100 may periodically prompt theuser to update the user's record of physical characteristics. Forexample, the fitness tracking device 100 may ask the user to input anupdated weight once a week, or once a month, or other suitableintervals. In other embodiments, the fitness tracking device 100 mayperiodically poll the user's cloud-based data storage service account orpoll the user's companion device 300 for updated information, or thefitness tracking device 100 may be configured to receive pushnotifications of updated physical characteristic information from thecloud-based data storage service, the companion device 300, or otherdevices.

At block 420, a default PAL may be set for the user. The PAL is a ratingsystem, such as a 0-7 scale, or the 0-10 scale shown below in Table 1,or a scale that includes more than eleven levels. In the example ofTable 1, a PAL score of 0 represents a lowest physical activity level(e.g., avoids walking or exercise), and a PAL score of 10 (or higher, insome embodiments) represents a highest physical activity level (e.g.,over seven hours per week of heavy physical exercise). The PAL ratingsystem may be based in part on the Physical Activity Rating (PA-R)rating system on a 0-7 scale. See Andrew S. Jackson et al., “Predictionof Functional Aerobic Capacity Without Exercise Testing,” Medicine andScience in Sports and Exercise, Vol. 22.6 (1990), pp. 863-870, which ishereby incorporated by reference in its entirety. The PAL scores anddescriptions in Table I are examples that may provide a parameter to anenergy expenditure model for calorimetry, such as a model based at leastin part on the research by Jackson et al. In other embodiments, otherranges of physical activity levels may be used, or other techniques forapplying PAL scores in an energy expenditure model may be used.

TABLE I Physical Activity Level (“PAL”) Scoring Sheet PAL ScoreDescription of Corresponding Level of Activity 0-1 point: Does notparticipate regularly in programmed recreation, sport, or physicalactivity:  0 points Avoids walking or exercise (e.g., always useselevators, drives whenever possible instead of walking)  1 point Walksfor pleasure, routinely uses stairs, occasionally exercises sufficientlyto cause heavy breathing or perspiration 2-3 points: Participatesregularly in recreation or work requiring modest physical activity (suchas golf, horseback riding, calisthenics, gymnastics, table tennis,bowling, weight lifting, or yard work):  2 points 10-60 minutes per weekof aforementioned activities  3 points Over 1 hour per week ofaforementioned activities 4-7 points: Participates regularly in heavyphysical exercise (such as running or jogging, swimming, cycling,rowing, skipping rope, or running in place), or engages in vigorousaerobic-type activities (such as tennis, basketball, or handball):  4points Runs less than 1 mile per week or spends less than 30 minutes perweek in comparable physical activity  5 points Runs 1-5 miles per weekor spends 30-60 minutes per week in comparable physical activity  6points Runs 5-10 miles per week or spends 1-3 hours per week incomparable physical activity  7 points Runs more than 10 miles per weekor a comparable physical activity 8-10 points: Participates in heavyphysical exercise for a significant amount of time per week:  8 points3-5 hours per week of “heavy physical exercise”  9 points 5-7 hours perweek of “heavy physical exercise” 10 points Over 7 hours per week of“heavy physical exercise”

As shown in Table I, some levels may be inferred based on distance (ifdistance information is available), or they may be inferred based ontime. The parameters and descriptions for each level in Table I aremerely examples, and any suitable number of levels or suitabledistinguishing features of each level may be used. For example, PALlevel 5 could be adjusted to require running more miles (e.g., 2-5miles) or spending more time per week in comparable physical activity(e.g., 45-60 minutes).

The default PAL score may be set to, for example, 2 points, or 3 points.In some embodiments, a first default PAL score (e.g., 2) may be selectedfor a first user in a first locale, and a second default PAL score(e.g., 3) may be selected for a second user in a second locale. In otherembodiments, a default PAL score (e.g., 2 or 3) may be selected based atleast in part on a predetermined “typical” PAL score for a group ofusers with similar physical characteristics as the physicalcharacteristics obtained for the user at block 410.

The fitness tracking device 100 may be configured to select a defaultPAL score so as to avoid requiring or otherwise requesting the user toself-report a PAL score. For example, a nutritionist or physical trainermight provide a client with a survey to help assess the client's PALscore. Moreover, self-reported PAL scores may not necessarily beaccurate because the client may not necessarily be able to answer thesurvey questions accurately. By selecting a default PAL score, it maynot be necessary for the fitness tracking device 100 to present a newuser with a similar survey, which may decrease setup time for a newfitness tracking device 100 and improve the user's overall setup oronboarding user experience.

For a typical or average user, a default PAL score such as 2 may beaccurate, if the user engages in 10-60 minutes of modest physicalactivity. For other users, it is possible that a default PAL score suchas 2 may either overestimate or underestimate the actual physicalactivity level of the user. For example, the default score may be anunderestimate for an ultra-marathoner, who may have an actual physicalactivity level equivalent to a PAL score of 10 (or higher, in someembodiments). Similarly, the default PAL score may be an overestimatefor a user who does not exercise and avoids walking. In turn, thecalorimetry process may overestimate or underestimate the user's energyexpenditure throughout the day based on a model, such as the Jackson etal. model, when using an inaccurate PAL score. However, as explained indetail below, after the user begins wearing the fitness tracking device100, the fitness tracking device 100 may be configured to recalibratethe calorimetry model within a short period of time by, e.g., inferringupdated PAL scores, regardless of whether the user engages in exerciseor what type of physical activity the user performs.

At block 440, the fitness tracking device 100 may determine whether theuser is engaged in a session of moderate to vigorous physical activity.If the user is determined not to be engaged in a session of moderate tovigorous physical activity (e.g., the user is engaged in modest orotherwise low-intensity physical activity, or the user is sedentary),calibration method 400 may return to block 430. If the user isdetermined to be engaged in a session of moderate to vigorous physicalactivity (e.g., running), calibration method 400 may proceed to block450.

It may be understood that, in some embodiments, a substantial amount oftime may pass during calibration method 400. For example, a first usermay go for a run (i.e., proceeding to block 450) within minutes ofturning on the fitness tracking device 100 for the first time, whereas asecond user may not go for a run for several months (if ever) afterturning on the fitness tracking device 100 for the first time. In theinterim, the fitness tracking device 100 may continuously or otherwiseperiodically update each user's individualized PAL score based at block430 as described below.

After performing recalibration at either block 430 or block 450,calibration method 400 may return to block 440 to reassess whether theuser is engaged in moderate to vigorous physical activity. Thus, thefitness tracking device 100 may continue to recalibrate or otherwiseadjust one or more energy expenditure models for calorimetry as thefitness tracking device 100 collects more data about a user's fitnessand activity levels or as the user's fitness or activity levels changeover time.

Calibration—Physical Activity Level Score Recalibration

As previously explained, an energy expenditure model may take intoaccount a physical activity level (PAL) score of a user to performcalorimetry processes more accurately. The fitness tracking device 100may collect data on the user's activity, such as an output from apedometer function or other activity classifier, to update the PAL scorefor a user.

FIG. 5 depicts a block diagram of a calibration technique for inferringphysical activity level in accordance with an embodiment of the presentdisclosure. As shown in FIG. 5, the fitness tracking device 100 mayaccess a step database 510. The step database 510 may be stored in amemory module of the fitness tracking device 100, or the step database510 may be accessible from another device or a cloud-based storageservice via interface module 240 (FIG. 2) of the fitness tracking device100.

The step database 510 maintains records of data about a user's activity.In a particular example, the step database may store records based on anestimated number of steps that the user has taken for each day over asequence of days based on the output of a pedometer of the fitnesstracking device 100. In some embodiments, the pedometer of the fitnesstracking device 100 may be implemented using data from one or moremotion sensors in motion sensing module 220 (FIG. 2).

In some embodiments, a valid day aggregator 520 will receive activityinformation such as daily step count information from the step database510. The valid day aggregator 520 may determine a moving average formean steps per day 530 based on a rolling window (e.g., a seven-dayrolling window) of data from the step database 510. In some embodiments,if a smaller rolling window of time is preferred, or if fewer than sevendays are available during the first week of use, a smaller rollingwindow may be used, such as 3 days, or 5 days.

In some embodiments, the valid day aggregator 520 will disregard recordsfrom the step database 510 that the valid day aggregator 520 maydetermine to be “invalid days.” For example, if the user forgets to wearthe fitness tracking device 100 for most of the day, or if the fitnesstracking device 100 is turned off or a battery of the fitness trackingdevice 100 is drained during most of a day, the total step count forthat day may be artificially low. The valid day aggregator 520 maydiscard a total step count for a day if the total step count is lessthan a threshold number of steps (e.g., fewer than 500 steps, or fewerthan 1,000 steps).

Thus, if the size of the rolling window is seven days, but the userforgot to wear the fitness tracking device 100 for one of the mostrecent seven days, the valid day aggregator 520 may discard that dayfrom its computation and, instead, use the total step count from theeighth most recent day to provide a total of seven “valid days” to beaggregated. The valid day aggregator 520 may output a mean steps per day530 that was computed using only valid days because the valid dayaggregator 520 may have filtered out the invalid days within the rollingwindow of time.

The fitness tracking device 100 may use the mean steps per day 530determined by the valid day aggregator 520 to lookup a corresponding PALscore 550 from a table, such as a one-dimensional user-specific table540, described in more detail herein with reference to Table II below:

TABLE II One-Dimensional Look-up Table Minimum Mean Steps per DayCorresponding Estimated PAL Score 0 0 points 1,000 1 point  2,000 2points 3,800 3 points 5,000 4 points 6,500 5 points 9,000 6 points11,000 7 points 14,000 8 points 17,000 9 points 20,000 10 points 

Table II shows a one-dimensional table (e.g., the one-dimensionaluser-specific table 540) in accordance with an embodiment of the presentdisclosure. The one-dimensional user-specific table 540 correlates aminimum (or range of) mean steps per day to a particular PAL score. Inthe example one-dimensional user-specific table 540 shown in FIG. 6,each PAL score 0 to 10 has a corresponding minimum number of steps. Forexample, a user would need to have at least 5,000 mean steps per day forthe fitness tracking device 100 to estimate that the user has a PALscore of 4 if using Table II. Similarly, if the mean steps per day 530is greater than or equal to 20,000 steps, the user's PAL score will beupdated to 10, according to the one-dimensional user-specific table 540shown in Table II. The range of PAL scores, as well as the default rangeof steps correlated to each PAL score, may vary in other embodiments.

In some embodiments, each range of steps is estimated based on theactivity level associated with each PAL score. The total number of stepsS_(total) for the low end and high end of each range is a sum of thesteps attributable to base-level activity (i.e., daily living activity)S_(base), steps attributable to modest activity S_(mod), and stepsattributable to heavy exercise S_(heavy):S _(total) =S _(base) +S _(mod) +S _(heavy)  (Eq. 1A)

The fitness tracking device 100 may assume, at least initially, thatbase activity and modest activity correlates to a typical walkingcadence C_(w) (e.g., 100 steps/min, or 120 steps/min) or a typicalwalking step length or stride length L_(w) (e.g., 0.6 m/step, or 0.8m/step), and that heavy activity correlates to a typical running cadenceC_(r) (e.g., 150 steps/min) and a typical running step length or stridelength L_(r) (e.g., 1 m/step). These default or typical values may bescaled or otherwise adjusted up or down according to an individualuser's typical cadence while walking or running and stride length whilerunning. For example, the fitness tracking device 100 may be configuredto provide a more accurate individualized stride length based on theuser's height. In some embodiments, the fitness tracking device 100 maybe configured to request the user's height from the user or otherwiseobtain the user's height from, for example, the companion device 300.

Thus, total steps S_(mod) during base activity or modest activity may becomputed as a product the time t spent engaged in base activity ormodest activity with a typical cadence C_(w) (for the portion of baseactivity or modest activity measured based on time) plus the number ofsteps based on walking stride length (for the portion of base activityor modest activity measured based on distance d traversed):S _(base) =t·C _(w) +d/L _(w)  (Eq. 1B)S _(mod) =t·C _(w) +d/L _(w)  (Eq. 1C)

Additionally, total steps S_(heavy) during heavy activity may becomputed with a typical running cadence C_(r) (for the portion of heavyactivity measured based on time t spent performing the heavy activity)and/or a typical running stride length L_(r) (for the portion of heavyactivity measured based on distance d traversed):S _(heavy) =t·C _(r) +d/L _(r)  (Eq. 1D)

For example, PAL score 3 corresponds to at least 60 minutes (i.e., atime-based parameter) of modest physical activity (i.e., anactivity-based parameter). The fitness tracking device 100 may assume,at least initially, that modest physical activity correlates to atypical walking cadence (e.g., 100 steps/min).

Thus, in this example, the amount of base activity for PAL score 3 maybe assumed to be a certain amount of time (e.g., approximately 30min/day of base activity, or in other embodiments, e.g., 45 minutes/dayof base activity). Thus, in this example, a minimum means steps/day mayaccount for steps attributable to base activity, which may be computedby multiplying the time of 30 min/day by the default cadence of 100steps/min, which equals 3,000 steps/day.

Similarly, in this example, the minimum mean steps per day for PAL score3 may include steps attributable to modest activity, which may becomputed by multiplying by the low-end time of 60 minutes/week by thedefault cadence of 100 steps/min, which equals 6,000 mean steps/week (orapproximately 800 steps/day).

In this example, PAL score 3 is assumed to include no amount of heavyexercise, so S_(heavy) equals 0, and S_(total) equals approximately3,800 steps/day. This value is reflected as the low end total number ofsteps needed for a user to qualify for a PAL score of 3 according toTable II.

In another example, PAL score 5 may be assumed to include at least: 1)60 min/day of base activity at 100 steps/min, or 6,000 steps/day, plus2) 30 min/week of modest exercise at 100 steps/min, or approximately 400steps/day, plus 3) 1 mile/week of heavy exercise, or approximately 100steps/day, for a total of at least 6,500 (mean) steps/day. This valuecorresponds to the minimum means steps per day correlated to PAL score 5using Table II.

Similar step-based estimations and computations may be similarly made togenerate the low and high ends of each step range for each PAL score.The parameters (e.g., cadences and stride lengths) may be calibrated orotherwise adjusted to the individual user, and these parameters may beused for both generating the one-dimensional user-specific table 540 aswell as for computing the user's steps throughout the day (e.g., by apedometer, or other activity-tracking functions, based on the motionsensing module 220 of the fitness tracking device 100).

FIG. 7 depicts another block diagram of a calibration technique forinferring physical activity level in accordance with an embodiment ofthe present disclosure. In FIG. 6, pedometer calibration module 610 maycalibrate the pedometer functionality of the fitness tracking device100. Additionally, pedometer calibration module 610 may outputindividualized or otherwise calibrated cadence and step length valuesfor walking and running (620), which may be used to generate theone-dimensional user-specific table 540 (e.g., Table II above).

Pedometer calibration may account for differences among users. Forexample, consider one runner who has a relatively high running cadencewhen compared to a second runner who has a relatively low runningcadence. If both runners run for one hour, the first runner with thehigher cadence will take more steps than the second runner with thelower cadence. Consequently, in some embodiments, the first runner withthe higher cadence may be expected to take more steps to qualify for agiven PAL score than the second runner.

In some embodiments, the fitness tracking device 100 may determine auser's PAL score based on a total number of steps that does notdifferentiate between which steps originated from base, modest, or heavyactivity, or whether the activity was measured based on time ordistance. In this case, the fitness tracking device 100 will considerthe aggregated number of steps as a single dimension of information forcorrelating activity to a PAL score.

In other embodiments, additional accuracy may be achieved bydifferentiating among the different activity sources to more closelycorrelate with the descriptions of each PAL score, which alsodifferentiate among different types of activity. The aggregated numberof steps may not indicate the sources of the steps, such as how manysteps can be attributed to running as opposed to lower intensityactivities such as walking Consequently, two users with the sameaggregated step count but who engage in different physical activitiesmay have different physical activity levels. As explained below, thefitness tracking device 100 may be configured to select or adjust auser's PAL level by considering information about the user's activitiesin addition to the user's aggregated step count.

FIG. 7 depicts another block diagram of a low-intensity calibrationtechnique in accordance with an embodiment of the present disclosure. Asshown in FIG. 7, an activity database 710 provides additional records ofuser data to the valid day aggregator 520. The activity database 710 maybe a part of the steps database 510, or it may be a separate database.The activity database may include a table with a row for each day. Eachrow in the table, or database record, may track various information,including, but not limited to, time spent or steps taken in base (or“background”) activities; time spent, distance traversed, or steps takenin modest activity; and time spent, distance traversed, or steps takenin heavy activity. In some embodiments, the pedometer functionality oranother activity classifier module may be configured to distinguishamong base activity, modest activity, and heavy activity. In otherembodiments, the fitness tracking device 100 may be configured to acceptuser input to determine when a user is going to be “in-session” for aparticular activity, such as going for a run or a bike ride, and thefitness tracking device 100 may record this information accordingly inthe activity database 710.

In some embodiments, the valid day aggregator 520 may use informationfrom both the steps database 510 and the activity database 710 toprovide two (or more) dimensions of data to look up PAL scores in auser-specific table of two-dimensions (or more). FIG. 7 depicts thevalid day aggregator 520 outputting both the mean steps per day 530 aswell as a mean activity time per day 730, both of which may bedetermined based on a rolling average as described above with referenceto FIG. 5.

Both the mean steps per day 530 and the mean activity time per day 730may be used as input to a two-dimensional user-specific table 740 (e.g.,Table III below). The two-dimensional user-specific table 740 may alsobe calibrated according to the pedometer calibration module 610 and theindividualized cadence and step length values 620 to customize thetwo-dimensional user-specific table 740 to a specific user, similarly asexplained above with reference to FIG. 6. The look-up function into thetwo-dimensional user-specific table 740 (e.g., Table III below) may beconfigured to output the PAL score that correlates to both dimensions ofinput data:

TABLE III Two-Dimensional Look-up Table Low Activity MinimumCorresponding High Activity Mean Estimated Corresponding Steps PAL ScoreMinimum Mean Estimated PAL Score per Day (Low Activity) Steps per Day(High Activity) 0 0 points 0 0 points 1,000 1 point  500 2 points 2,0002 points 1,500 3 points 3,800 3 points 2,500 4 points 5,000 4 points4,000 5 points 6,500 5 points 5,500 6 points 9,000 6 points 9,000 6points 11,000 7 points 11,000 7 points 14,000 8 points 14,000 8 points17,000 9 points 17,000 9 points 20,000 10 points  20,000 10 points 

Table III (above) shows an example of a two-dimensional user-specifictable 740 in accordance with an embodiment of the present disclosure.Like the one-dimensional user-specific table 540 described withreference to Table II, the two-dimensional user-specific table 740 alsocorrelates a range of mean steps per day (i.e., step ranges 900-911)with a PAL score of 0-11. However, in this example of thetwo-dimensional user-specific table 740, some step ranges overlap withother step ranges. For example, step range 900, which correlates to PALscore 0, ranges from 2,180 to 4,300 steps, while step range 901, whichcorrelates to PAL score 1, ranges from 3,500 to 4,500 steps. Thus, meansteps per day 530 may be insufficient to determine a unique step rangeand output a unique PAL score. For example, if mean steps per day 530equals 4,000 steps, that value by itself may indicate either PAL score 0or PAL score 1.

In this example, the two-dimensional user-specific table 740 can alsoaccount for activity, such as activity type and quantity or meanactivity time per day 730, as a way to look at one or more additionalvariables to break a tie between overlapping ranges. As shown in FIG. 9,a way to distinguish between the minimum steps needed for PAL score 1(e.g., 1,000 steps for relatively low activity versus 500 steps forrelatively high activity) along the second dimension of activity may beto determine, e.g., whether the user walked up at least one flight ofstairs. A way to distinguish between overlapping step ranges for PALscore 2 may be to determine, e.g., whether the user spent a (movingdaily average) of at least ten minutes on at least one walk or modestbike ride. As another example, a way to distinguish between overlappingstep ranges may be to determine, e.g., whether the user spent any time(greater than zero minutes over a moving daily average) on at least oneheavy activity, such as a run, bike ride, or other heavy exercisesession.

In some embodiments, the two-dimensional user-specific table 740 may usedifferent conditions for distinguishing between relatively low or highactivity depending on which PAL scores need to be distinguished (e.g.,flights of stairs to distinguish between low PAL scores as opposed togoing for a run to distinguish between higher PAL scores). Also, in someembodiments, the two-dimensional user-specific table 740 may includesome non-overlapping step minimums or step ranges for which the level ofactivity or other dimension of data may not affect to the estimated PALscore. For example, in Table III above, if a user reaches approximatelyat least 6,500 mean steps per day (corresponding to a PAL score of atleast 6), the activity metrics may not change the estimated PAL score.In other embodiments, the two-dimensional user-specific table 740 mayinclude overlapping step ranges for every PAL score.

In some cases where step ranges overlap, a user with greater activitytime at higher levels of intensity may receive a higher PAL score evenif they have fewer total steps than a second user with a lower PALscore. In some embodiments, if a user's activity indicates excessiveidle (or sedentary) time, the user may receive a lower PAL score.

As explained above, in some embodiments, accuracy may be improved byconfiguring the valid day aggregator 520 to determine PAL scores basedon moving averages (e.g., a seven-day moving average). Accuracy may alsobe improved by disregarding days with relatively low total step counts(e.g., days on which the fitness tracking device 100 recorded fewer than1,000 steps).

FIGS. 8A-8C illustrate the relative accuracy of different configurationof the valid day aggregator 520 for a hypothetical user whoseself-reported PAL (or PA-R) is 6. In this example, the hypothetical userrecorded daily total step counts over a 10-week period. Each figure ofFIGS. 8A-C includes a histogram (one of charts 800A-C) that illustrateshow frequently the calibration technique for inferring a physicalactivity level would estimate a particular PAL score, given a particularconfiguration of the valid day aggregator 520 (e.g., daily step count orweekly periodic average). Each chart 800A-C, the same underlying dailystep counts are analyzed, but each chart 800A-C applies a differentfilter to the data, such as daily step count (chart 800A) or a dailymoving average (chart 800B).

In FIG. 8A, chart 800A shows the distribution of PAL scores when using adaily step count. Valid day aggregator 520 (e.g., FIG. 5) is either notpresent, switched off, or otherwise configured to use the daily valuesof mean steps per day instead of computing a moving average or filteringout low-activity days (i.e., days during which the user may haveforgotten to wear the fitness tracking device 100). Chart 800A shows arelatively wide day-to-day variation in PAL scores, including severaldays rated as “0” when the user may have forgotten to wear the fitnesstracking device 100. In some embodiments a PAL score of 0 may bedistinguished from days when the device is not worn by estimating alevel of, e.g., negative 1. In some embodiments, an individual'sphysical activity level may be expected to remain relatively constant,but the user's day-to-day activities can fluctuate (e.g., a runner witha self-reported PAL score of 8 may only run every other day).Consequently, this user's daily step count may also fluctuate day-to-daybut remain relatively constant over longer time periods.

In FIG. 8B, chart 800B shows the distribution of PAL scores when using aseven-day daily moving average. Charts 800B shows a tighter distributionof estimated PAL scores than chart 800A, and the elimination of someoutliers, with the frequency of selecting the self-reported PAL score of6 (or PAL scores close to 6, e.g., 5) has increased. In someembodiments, the moving average may be computed less frequently. Forexample, the moving average may be estimated on a weekly basis (e.g., aweekly periodic moving average). In some embodiments, the weekly periodmoving average may be likely to provide a similar distribution ofestimates as the daily moving average example discussed above withreference to FIG. 8B.

In FIG. 8C, chart 800C shows the distribution of PAL scores when thevalid day aggregator 520 is not only calculating a daily moving averagebut also filtering out low-activity days when the user may haveforgotten to wear the device. Chart 800C provides the tightestdistribution, reflecting the most consistent and accurate estimation ofPAL scores compared to charts 800A-C in FIGS. 8A-C.

Calibration—Heart Rate and Work Rate-Based Calibration

Calibration techniques for inferring physical activity level such asthose described above may be helpful when exercise-based heart rate orwork rate data is not available. Models such as the Jackson et al. modelmay use physical activity scores such as PAL or PA-R in conjunction withphysical characteristics such as age, weight, and sex to estimateaerobic capacity ({dot over (V)}O₂max) without exercising. However, insome cases a user may find that an exercise-based calibration may allowa device such as fitness tracking device 100 to estimate {dot over(V)}O₂max more accurately than the non-exercise or otherwise PALinference-based calibration techniques such as those described above,and more accurate estimates of {dot over (V)}O₂max may allow for moreaccurate estimates of energy expenditure when exercising.

In contrast to measuring {dot over (V)}O₂max in a lab setting, {dot over(V)}O₂max can be estimated during suitable “submaximal” exercises (e.g.,running for ten minutes). Although the user may not reach maximumaerobic capacity, heart rate or work rate information collected duringsubmaximal testing may allow for the user's {dot over (V)}O₂max to beestimated. Monitoring heart rate measurements (in, e.g., beats perminute or bpm) in conjunction with work rate measurements (in, e.g.,joules per second or watts) provides data on which regression analysismay be performed to extrapolate an individual's parameters such as {dotover (V)}O₂max.

In some embodiments, a method for estimating {dot over (V)}O₂max fromobservations of heart rate and work rate inputs may involve two stages.The first stage may be an estimation process, such as a least squaresestimation process, or a running least squares estimation process. Inthe example of a running least squares estimation process, it may bepossible to update the estimate for each new data point withoutrerunning many prior computations.

The second stage may be a robust outlier rejection strategy, such as aconstrained iteratively reweighted least squares solution. In thisexample, the solution to the estimate may be constrained based on aninitial state (e.g., the estimate based on the running least squaresestimation process described above). In some embodiments, the constraintmay assume that the relationship between heart rate and oxygenconsumption is linear. In some embodiments, this assumption of linearitymay be extended below conventional thresholds, e.g., the assumption maybe extended below a typical assumption of approximately 40%. In someembodiments, the iteratively reweighted solution may seek a minimumL^(p) norm for, e.g., p=[1.0, 1.5].

In some embodiments, to improve the reliability of data used forcalibration, and to filter out some outliers, this calibration methodmay require one or more conditions to be met. For example, thiscalibration method may require Fraction of Heart Rate Reserve (FHR) tobe less than half (e.g., less than 0.5). FHR may be defined as the ratioof how close an individual's current heart rate is to his or her maximumheart rate (e.g., HRmax−HR) to the individual's heart rate range (e.g.,HRmax−HRmin):FHR=(HRmax−HR)/(HRmax−HRmin)  (Eq. 2)

FHR may range from 0 to 1 for any individual. When an individual's FHRis close to 0, it indicates that the user's heart rate is close to theindividual's maximum heart rate. Similarly, when an individual's FHR isclose to 1, it indicates that the individual's heart rate is close tothe individual's minimum heart rate. Thus, if the individual's FHR isless than 0.5 (i.e., the user's heart rate is closer to maximum heartrate than to minimum heart rate). Consequently, requiring theindividual's FHR to be, for example, less than 0.5 may help ensure thatthe individual is engaged in an active, moderate to high intensityactivity that is more likely to be suitable for heart rate and workrate-based calibration.

Similarly, this calibration method may require work-rate based METs toexceed a threshold value (e.g., 5 METs, or 10 METs). Furthermore, insome embodiments, the calibration process may restrict measurementstaken while a user is accelerating (e.g., speeding up or slowing down).For example, this calibration method may compare a current measurementof work rate-based METs to a running average or moving average of workrate-based METs (e.g., the change in METs may be required to be with 1MET of the running average).

In some situations, a user may run at a relatively constant speed andmaintain a relatively constant heart rate for the duration of a run. Inthe absence of any constraints, having approximately one data point, ora tight cluster of similar data points, may not provide enoughinformation to perform a linear regression (e.g., a least squaresestimation technique). In some embodiments, the calibration techniquemay infer at least one additional data point, which may indicate theuser's minimum heart rate and basal metabolic rate. Including thisinferred data point ensures that: 1) the user's individualized linearheart rate model will include the user's basal metabolic rate at minimumheart rate, and 2) the user's individualized linear heart rate modelwill have a positive slope within a reasonably constrained range.

In some situations, the user may progress through several local steadystates. For example, the user may run at 5 mph for the first fiveminutes, then speed up to 10 mph for the second five minutes. In thissituation, the calibration technique may have at least two reliable datapoints or clusters of data points with which to perform the leastsquares estimation process. In some embodiments, the calibrationtechnique will still include the basal metabolic rate and minimum heartrate because it may still be advantageous to ensure the model runsthrough that point, and it gives the model increased accuracy with yetanother data point to include in the regression.

The following section and set of equations indicate how this combinedheart rate and work rate-based estimation process may proceed:

Let h₀=resting heart rate (or minimum heart rate) of an individual, andlet h_(k), k=1, 2, . . . , N denote the obtained heart rate samples,which may be represented by a vector A.

Let m₀=the basal metabolic rate estimated for the individual using,e.g., the Mifflin equation, or the Harris-Benedict equation, or anaverage of the Mifflin and the Harris-Benedict equations. Let m_(k), fork=1, 2, . . . , N denote the work rate-estimated METs obtained at eachcorresponding time as the heart rate samples, which may be representedby a vector B.

For each heart rate measurement, there is also a weight (e.g., avariance or a confidence level) associated with each heart ratemeasurement, which may be represented as a diagonal matrix W.

The initial estimate (e.g., the running least squares estimate) may beindicated by min∥W(Ax−b)∥₂, and constrained by x:[h₀1]x=m₀, whereby theconstraint may compel the least squares estimation to include the point(h₀, m₀) (the resting heart rate and basal metabolic rate).

This initial estimate based on a running least squares estimationprocess may be refined further using an iteratively reweighted leastsquares process. Upon convergence, the user's {dot over (V)}O₂max may beestimated to be x₁(HRmax−h₀)+m₀.

In some embodiments, this {dot over (V)}O₂max may undergo furthertesting before determining that it should be accepted as a validcalibration. For example, the calibration method may compare theestimated {dot over (V)}O₂max to a ratio of the total METs based on workrate measurements to the % {dot over (V)}O₂max predicted from the heartrate measurements, whereby % {dot over (V)}O₂max=f(FHR), and where f(x)is a machine-learned algorithm for predicting % {dot over (V)}O₂max fromFHR. This ratio is effectively another estimate of {dot over (V)}O₂max.If the divergence between the HR-WR based {dot over (V)}O₂max is withina threshold of the ratio estimate of {dot over (V)}O₂max, the HR-WRbased {dot over (V)}O₂max may be accepted as the user's (calibrated){dot over (V)}O₂max.

In some embodiments, this divergence process may be used to compare asubsequently estimated {dot over (V)}O₂max to a previously accepted {dotover (V)}O₂max. If the two values are within a threshold divergence ofone another, it may be assumed that the previously estimated {dot over(V)}O₂max was valid. In a situation in which the new {dot over (V)}O₂maxhas diverged from the previous {dot over (V)}O₂max beyond a thresholddivergence, it may be an indication that the user's {dot over (V)}O₂maxhas changed, and the new {dot over (V)}O₂max may be accepted as arecalibrated value.

As explained below, the fitness tracking device 100 may further be ableto take into account data such as automatic activity classificationdata, user input about when a user is beginning or ending an exercisesession and what type of exercise (e.g., running or cycling) the userwill be performing, and sensor information from other devices such asGPS location data from the companion device 300 via interface module 240(FIG. 2).

FIG. 9 shows a high-intensity calibration method in accordance with anembodiment of the present disclosure. At block 910, an activity contextmay be inferred or provided. For example, in some embodiments, anactivity classifier may automatically detect the type of activity (i.e.,activity context) that a user is performing. In other embodiments, thefitness tracking device 100 may be configured to receive informationfrom the user about the activity context that the user is performing orintends to begin performing (e.g., the user indicates that the user is“in session” for running) Once an activity context has been inferred,provided, or otherwise detected, the method may proceed to block 920.

At block 920, a determination is made as to whether the previouslydetermined activity context may be suitable for HR-WR calibration (960).For example, HR-WR calibration 960 may be most accurate for vigorous,intense exercises, such as running for at least ten minutes.

For a given time period, the inferred activity context may not be ableto predict whether a context that is suitable for calibration may beinterrupted before enough time has elapsed or enough HR-WR data has beencollected to calibrate accurately. Thus, the activity context may beinferred continuously, or at periodic intervals, at block 910 to detectwhether the activity context may have changed. If a change is detected,and it is determined at block 920 that the new activity context is notsuitable for HR-WR calibration 960, the calibration method may abort orrestart.

At block 930, heart rate context may be obtained continuously or atperiodic intervals (e.g., once per minute, or once per three minutes,etc.). For example, the fitness tracking device 100 may obtain a heartrate context by receiving heart rate data from a PPG sensor in the heartrate sensing module 220 (FIG. 2). In some embodiments, the frequency forchecking the user's heart rate may be increased if the activity contextindicates that the user is performing an activity suitable for HR-WRcalibration 960. A confidence level for a heart rate measurement at agiven time period may also be determined to facilitate HR-WR calibration960.

At block 940, work rate context may be obtained continuously or atperiodic intervals (e.g., once per minute, or once per three minutes,etc.). For example, the fitness tracking device 100 may obtain motiondata from an accelerometer or other sensor in motion sensing module 230(FIG. 2). In some embodiments, work rate context may also be derivedusing altitude information from an altimeter or location informationfrom a GPS sensor, and these sensors may optionally be located in acompanion device 300 that is also strapped or otherwise worn by the userduring the intense calibration activity. For example, while running orcycling, GPS information may convey a distance traversed to assess theuser's speed. Similarly, altimeter information may convey informationabout changes in grade to assess the power needed to traverse a givendistance at a given grade.

In other embodiments, the fitness tracking device 100 may be configuredto receive annotations of the work in watts. For example, if the user isexercising on a stationary bike, GPS information will not be useful fordetermining work rate. Additionally, if the stationary bike simulateschanges in grade, altimeter information will not be useful fordetermining changes in work rate due to changes in grade. Thus, in agraded stationary cycling calibration activity, during which GPS andaltimeter information may not be useful, watt annotation from the usermay be useful.

At block 950 a calorimetry process may be performed to estimate caloriesburned based on the work rate data from the work rate context from block940. The work rate model applied for the work rate calorimetry processat block 950 may vary depending on the received activity context. Forexample, the work rate calorimetry model for running may be differentfrom the work rate model for cycling, as described in more detailherein.

At block 960, HR-WR calibration may be performed if there is anindication from block 920 that the current activity context is suitablefor calibration. HR-WR calibration 960 may use both the heart ratecontext from block 930 and the work rate estimated calories informationfrom block 950 (which was based on the motion and other data from thework rate context determined at block 940 and the activity contextdetermined at block 910).

At block 970, the conclusion of the intense activity suited forcalibration, and if the intense activity endured sufficiently long(e.g., a ten-minute run) to collect sufficient heart rate or work ratedata to estimate {dot over (V)}O₂max as explained above, the estimated{dot over (V)}O₂max may be used to generate a model for energyexpenditure based on the linear relationship between heart rate and {dotover (V)}O₂ described above. This energy expenditure model may be moreaccurate than a model determined based on heart-rate measurements alonewithout the benefit of work rate measurements, especially for heart ratedata for time periods when the confidence level is relatively low. Thisheart-rate energy expenditure model estimates a user's {dot over(V)}O₂max to be used by subsequent calorimetry processes. Additionally,these parameters may be estimated more accurately than thelow-intensity, non-exercise based calibration, which estimates physicalactivity level to estimate {dot over (V)}O₂max instead of heart rate andwork rate measurements during exercise.

This process may also be repeated to recalibrate or refine thecalibration of the energy expenditure model by initiating anotherintense activity suited for calibration.

By generating a more accurate calorimetry model to estimate energyexpenditure using heart rate (or a combination of heart rate and workrate) during exercise, the fitness tracking device 100 may be configuredto provide the user with a more accurate estimate of calories burnedduring exercise, which in turn provides the user with a more accurateestimate of total energy expenditure throughout the day over a varietyof activities.

Calorimetry—Overview

Once a device such as the fitness tracking device 100 has beencalibrated, it may perform calorimetry, i.e., measuring a user's energyexpenditure (e.g., calorie burn) over time and across a variety ofactivities. Because the fitness tracking device 100 may be configuredwith a default calibration (i.e., a default PAL score combined withphysical characteristics that may be provided during a brief setup orinitialization phase), the fitness tracking device 100 may begintracking energy expenditure immediately or nearly immediately.

The fitness of the user may change over time, the device may berecalibrated, or the calibration of the device may be refined (e.g., byprocesses described above, such as a calibration technique for inferringphysical activity level, or an active work rate and heart rate-basedcalibration technique). As the fitness tracking device 100 refines andimproves upon its understanding of the user's individualizedcharacteristics, such as estimates for maximum heart rate and {dot over(V)}O₂max, the calorimetry processes of the fitness tracking device 100may become increasingly accurate over time, and remain accurate even ifthe user's fitness level changes over time as well.

Developing a heart rate-based energy expenditure model may be helpfulfor calorimetry processes that measure energy expenditure for moderateto vigorous activities. Even in these cases, an algorithm or model thatworks well for running may not work as well for cycling (e.g., the METsfor running may be different from the METs for cycling). Additionally,some activities may rely more on work rate calculations or a combinationof work rate calculations with heart rate monitoring. The followingsection describes in detail various embodiments in which calorimetryprocesses may use a fusion of heart rate and work rate data to estimateenergy expenditure more accurately across different activities such asrunning and cycling.

For some activities, such as using elliptical trainers or step machines,which provide a load or resistance that affects energy expenditure,individuals may experience an elevated heart rate as the resistance ofthe machine increases. The sections that follow also describe accurateheart-rate based calorimetry models based on an estimated load.

For some activities, such as weight lifting, which are not pacedactivities such as running, individuals may experience frequent changesin heart rate as they engage in the onset of an activities (e.g., anindividual repetition or a set of repetitions), and cool-down periods(e.g., moments of time between repetitions or a brief rest between setsof repetitions). The sections that follow also describe accurate heartrate-based calorimetry models for intermittent activities.

For other, lower heart-rate activities, such as walking, a heartrate-based calorimetry model may not be as reliable as some othermethods. For these activities, a pedometry-based calorimetry model andprocess may be more accurate or more reliable.

Additionally, users also burn calories while sedentary (e.g., sleeping,sitting, standing, or otherwise “at rest”). However, the energyexpenditures vary among these activities and across individuals (e.g.,sitting requires fewer METs than standing). Especially for users who mayspend a substantial portion of their day sitting (or standing), accurateday-long calorimetry processes may benefit from being able todistinguish between whether a user is sitting or standing automatically,and adjust the calorimetry model accordingly. Even a relatively smalldifference in METs between sitting and standing may add up to be asubstantial difference in energy expenditure over the course of a dayfor a relatively sedentary individual.

Calorimetry—Heart Rate and Work Rate-Based Calorimetry

As explained below with reference to FIG. 10, and similar to heart rateand work rate-based calibration described above with reference to FIG.9, combining heart rate and work rate information may provide moreaccurate calorimetry than either method on its own.

FIG. 10 shows a high-intensity calorimetry method in accordance with anembodiment of the present disclosure. At block 1010, a current heartrate (e.g., beats/min) may be obtained from the heart rate sensingmodule 210 (FIG. 2).

In addition to providing the current heart rate, the heart rate sensingmodule 210 may also provide a confidence level (e.g., σ² _(HR))associated with the current heart rate. The confidence level is aquantified indicator of how accurate the heart rate sensing module 210may be. For example, if the heart rate sensor input is relatively noisy,the confidence level for the measured heart rate may be relatively low.In some embodiments, the confidence level originating from the heartrate sensor (e.g., a PPG sensor) may be adjusted depending on othermeasured conditions. For example, if it is known that heart rate sensorvalues are relatively noisy at higher speeds, such a property may beincorporated into the computation of the confidence time series.

The current heart rate and confidence level may be used to compute anormalized heart rate (NHR), (or fraction of heart rate reserve (FHR),as explained above):NHR=(HR_(max)−HR)/(HR_(max)−HR_(min))  (Eq.2A)

NHR may be used as an input to a heart-rate based calorimetry modelduring exercise to estimate the current (or “instantaneous”) rate ofenergy expenditure (or calorie burn) during exercise.

The individualized values for minimum and maximum heart rate (HR_(min)and HR_(max)) may have been previously estimated using one of thepreviously described calibration techniques. At block 1020, an estimateof energy expenditure based on a heart rate model may take NHR as inputand output the user's corresponding percentage of aerobic capacity (%{dot over (V)}O₂max). Because the user's individualized {dot over(V)}O₂max may have been previously estimated using one of the previouslydescribed calibration techniques, the heart rate model may convert %{dot over (V)}O₂max into METs and calories burned (i.e., energyexpended) according to the heart rate data.

Similarly, at block 1030, the work rate may be estimated using motiondata or related information. A confidence level for work rate may alsobe computed (e.g., σ² _(WR)). In some embodiments, work rate confidencemay be determined based on a relative accuracy of a GPS sensor for agiven piece location information. In some embodiments, a GPS sensor maybe included in the companion device 300 but not the fitness trackingdevice 100. If the companion device 300 is not available to communicateGPS sensor information to the fitness tracking device 100, heart rateand work rate confidence information (e.g., heart rate and work ratevariances) may not be available to apply a mixing function (e.g., aweighted, probabilistic average of heart rate and work rate based ontheir relative variances), which is explained in more detail below inreference to Equation 9.

At block 1040, an estimate of energy expenditure based on a work ratemodel and predetermined activity context may output calories burned(i.e., energy expended) according to the work rate data.

At block 1050, the heart rate-based energy expenditure (EE_(HR)) may be“fused” or otherwise combined with the work rate-based energyexpenditure (EE_(WR)). In some embodiments, the data may be fused usinga Bayesian probability framework. For example, the framework may beexpressed as determining the best estimate for energy expenditure (EE),given the work rate and heart rate estimates EE_(HR) and EE_(WR):

$\begin{matrix}\begin{matrix}{{P\left( {\left. {EE} \middle| {EE}_{HR} \right.,{EE}_{WR}} \right)} = \frac{{P\left( {{EE}_{HR},\left. {EE}_{WR} \middle| {EE} \right.} \right)}{P({EE})}}{P\left( {{EE}_{HR},{EE}_{WR}} \right)}} \\{= \frac{{P\left( {{EE}_{HR},{EE}} \right)}{P\left( {{EE}_{WR},{EE}} \right)}{P({EE})}}{P\left( {{EE}_{HR},{EE}_{WR}} \right)}}\end{matrix} & \left( {{{Eq}.\mspace{14mu} 2}B} \right)\end{matrix}$

The Bayesian framework expressed in Equation 6 assumes that the outputof the heart rate and work rate models are independent. If a uniformprior and normal densities are assumed for the following likelihoodfunctions . . .P(EE_(HR))˜N(EE,σ² _(HR))  (Eq.2C)P(EE_(WR))˜N(EE,σ² _(WR))  (Eq. 2D)

. . . then the maximum a posteriori estimate for EE may be given by thefollowing “mixing function”:EE=(σ² _(HR)EE_(WR)+σ² _(WR)EE_(HR))/(σ² _(HR)+σ² _(WR))  (Eq. 2E)

The variances σ² _(HR) and σ² _(WR) may correspond to the confidencevalues for the heart rate input from block 1010 and the work rate inputfrom block 1030, respectively. A decrease in the variance from onesensor (i.e., an increase in confidence) may cause the fused estimate ofEE computed with Equation 9 to tend toward the value of the sensor withthe decreased variance.

At block 1060, the energy expenditure estimate EE computed using themixing function at Equation 2E may be outputted.

Calorimetry—Heart Rate and Work Rate-Based Calorimetry for Cycling

As mentioned above, the work rate energy expenditure model used at block1040 may depend in part on the type activity (e.g., activityclassification). For example, in the case of cycling, an accurate workrate model may be based on a nonlinear combination of speed and grade.This model may estimate the total energy expended EE by estimating anamount of energy required to move a cyclist (e.g., the user whilecycling) through space.

The main sources of resistance are defined by the rolling resistance(P_(rr)), aerodynamic drag (P_(ad)), changes in potential energy(P_(plot)), and changes in kinetic energy (P_(kin)):P _(rr) =V _(g) C _(rr) mg cos(arctan(G))  (Eq. 3A)P _(ad)=ρ(T)C _(a)(V _(g) +V _(ω) cos(α))² V _(g)  (Eq. 3B)P _(pot) =V _(g) mg sin(arctan(G))  (Eq. 3C)P _(kin)=0.5(m+I/r ²)(V ² _(gf) −V ² _(gi))/(t _(f) −t _(i))  (Eq. 3D)P _(total) =P _(rr) +P _(ad) +P _(pot) +P _(kin)  (Eq. 3E)

In Equations 3A-E:

V_(g) is the ground speed of the cyclist, which in some embodiments maybe determined based on GPS sensor data.

C_(rr) is the coefficient of rolling resistance, which in someembodiments may be assigned a default value.

m is the combined mass of the bike and the cyclist. In some embodiments,the mass of the bike may be assigned a default value, and the mass ofthe cyclist may be determined based on the user's weight.

g is gravity, which may be assigned a default value of g_(n), the“standard” or average gravity at the Earth's surface (i.e., 9.80665m/s²).

G is the slope of the surface. In some embodiments, a default value(e.g., 0 radians) may be assigned. In other embodiments, data from oneor more motion sensors (e.g., an altimeter) may be used to estimate acurrent slope of the surface.

ρ(T) is a temperature-dependent air density. In some embodiments, adefault value (e.g., an average temperature) may be assigned. In otherembodiments, the fitness tracking device 100 or the companion device 300may be configured to obtain a current temperature value for the user'slocation (e.g., GPS sensor-based location). In other embodiments, thefitness tracking device 100 or the companion device 300 may include atemperature sensor.

C_(α) is the constant for aerodynamic drag, which may be assigned adefault value.

V_(w) is the wind speed, and α is the angle between the wind vector andthe cyclist's direction of travel or speed (e.g., the cyclist's velocityvector). In some embodiments, V_(w) and α may be assigned defaultvalues. In other embodiments, the fitness tracking device 100 or thecompanion device 300 may be configured to compute these parameters byobtaining a current wind speed and a current wind direction for theuser's location, as well as the cyclist's current direction of travel.

I is the moment of inertia of the wheels on cyclist's bike. In someembodiments, I may be assigned a default value.

r is the radius of the wheel. In some embodiments, r may be assigned adefault value.

V_(gf) and V_(gi) correspond to the final and initial velocities,respectively, when the cyclist accelerates.

These factors should not be considered exhaustive. Examples of otherfactors for which some embodiments may account in their models includedrive train losses, wheel bearing losses, and wheel rotation.

The actual energy expended by the cyclist may also depend on theirbio-mechanical efficiency (η):EE=η·P _(total)  (Eq. 3F)

In a case where not all of the constants are known, the model may beapproximated by:EE=V _(g)(α₁)mg cos(arctan(G))+(α₂)V _(g)+(α₃)V _(g) ²+(α₄)V _(g)³+(α₅)V _(g) mg sin(arctan(G))+(α₆)m(V ² _(gf) −V ² _(gi))/(t _(f) −t_(i))  (Eq. 3G)

In some embodiments, coefficients α₁-α₅ may be learned from a trainingset.

In some embodiments, additional improvements may be made to improve theaccuracy of the heart rate model or the work rate model. For example,temporal dynamics (e.g., a Kalman filter), or a determination of aregression function from the data using a more general model such as arandom forest or a neural network, may be applied the either or both ofthe heart rate model and the work rate model.

Calorimetry—Automatic Terrain Detection

Another example where additional improvements may be made to the cyclingwork rate model (and, in some embodiments, a pedestrian or running workrate model, etc.) is with a method for automatic terrain detection. Forexample, being able to distinguish between different types of terrain(e.g., cement, asphalt, dirt, gravel, etc.) may allow for updating thecycling work rate energy expenditure model to use a more accurate valuefor certain parameters, e.g., the coefficient of rolling resistance(C_(rr)), which may allow for a more accurate estimate of rollingresistance (P_(rr)), which, in turn, may allow for more accurateestimate of energy expenditure while cycling on a particular type ofterrain.

In some embodiments, automatic terrain detection may also improve thework rate model for pedestrian activities (e.g., running) by allowingthe work rate expenditure model to include an additional efficiencyparameter related to terrain. For example, some terrain types (or typesof surfaces) are easier to run on than others and may be more efficient.For example, a track may require less energy to maintain a given speedthan, e.g., a gravel trail. The efficiency of a given terrain in thecontext of running (or a comparable activity) may be expressed as anefficiency (or “correction”) term, such as a linear correction factorsuch as (a)(EE)+(b). In this example, determining the terrain may allowvalues for the (a) and (b) parameters (multiplicative and additivefactors, respectively) to be inferred as well.

FIG. 11 depicts a terrain detection method in accordance with anembodiment of the present disclosure. At block 1110, a “raw”accelerometer signal is provided to both block 1120 for determining anactivity context and block 1130 for performing Fast Fourier Transforms(FFTs) and frequency domain analysis. Because the accelerometer signalis “raw,” meaning that the signal has not been filtered or processedyet, a full frequency spectrum in the signal may be available foranalysis. The raw accelerometer signal may be provided from anaccelerometer included in the motion sensing module 220 (FIG. 2) of thefitness tracking device 100.

At block 1120 an activity context may be determined based on the rawaccelerometer signal from block 1110. In some embodiments, the rawaccelerometer signal may be analyzed to determine the activity context(e.g., running or cycling). In other embodiments, the fitness trackingdevice 100 may receive input from a user about the type of sessionactivity that the user may be performing. As explained in more detailbelow, the activity context information may be used at block 1180 tohelp infer the terrain type automatically, which may be performed basedon a machine learning model.

At block 1130, the raw accelerometer signal from block 1110 may betransformed to the frequency domain (e.g., with an FFT algorithm).

At block 1140, a low-pass filter (LPF) may be applied to theaccelerometer signal in the frequency domain. The LPF may be configuredto extract a “gait feature” at block 1150. The gait feature may be theportion of the frequency spectrum in the motion data from a humansource, as opposed to portions of the frequency spectrum that representnoise such as vibrations due to different types of terrain. The gaitfeature may appear as the dominant frequency at, for example, up to 4Hz, or up to 5 Hz. By subtracting the gait feature frequency range fromthe motion data (e.g., by performing source separation on the motiondata), the machine learning model may be applied at block 1180 to detectthe type of terrain automatically with greater accuracy.

Similarly, at block 1160, a high-pass filter (HPF) or a band-pass filtermay be applied to the accelerometer signal in the frequency domain. TheHPF may be configured to extract broad band features from the frequencyspectrum at block 1170, which may contribute to inferring the terraintype by the machine learning model at block 1180. For example, the broadband features from the frequency spectrum may include the residual noisein, e.g., the 5-20 Hz range, or the 7-25 Hz range, etc.

In addition to the residual noise at higher frequencies that may beattributable to the terrain, there may also be residual harmonics athigher frequencies that may be attributable to the (lower-frequency)gait feature. In this example, information about the gait featureobtained with the LPF may be used to subtract at least some of theresidual harmonics due to the gait feature from the higher-frequenciesobtained by the HPF.

At block 1180, the terrain type may be inferred using a machine learningmodel that accounts for activity context from block 1120, the gaitfeature frequency information from block 1150, and the broad bandfrequency information from block 1170. In some embodiments, the gaitfeature may represent a frequency signature attributable to the user'sactivity. After the frequency signature attributable to the user'sactivity has been subtracted out, the residual frequency spectrum mayconsist of noise induced by the terrain. The broad band frequencyspectrum may exhibit recognizable characteristics for recognizingdifferent types of terrain.

In some embodiments, the high-pass filtering, broad band featureextraction, or machine learning model may leverage existing processesperformed for activity context. For example, the same broad band roadnoise features that may be used by the activity context classificationprocess to identify a cycling activity context may also be used todetermine the type of terrain on which the user is cycling.

Also, in some embodiments, the machine learning model may benefit from afitness tracking device 100 that is worn on the user's wrist. Forexample, during a cycling activity, energy transferred from the terrainthrough the handlebars to the user's hands may reach the user's wristmore reliably that if a device with an accelerometer were positionedfarther away from the user's hands.

Calorimetry—Automatic Load Estimation

For certain types of activities, such as indoor exercise sessions (on,e.g., elliptical trainers, rowing machines, stepper machines, etc.),calorimetry accuracy may be improved by automatically estimating load(or “mechanical resistance”) from the equipment. For example, manyelliptical trainers may have a configurable load or resistance thatallows the user to adjust how much effort is needed to perform eachrepetition on the machine. The higher the load, the more effort isneeded, and therefore the energy expended to perform the work alsoincreases.

For example, energy expenditure on an elliptical trainer may be modeledusing the following equation:Elliptical EnergyExpenditure=(a)(counts/min)+(b)(counts/min)(load)+(c)  (Eq. 4A)

In Equation 16A, (a)-(c) are parameters that may be calibrated orotherwise adjusted for an individual user. Counts per minute(“counts/min”) represents the number of revolutions of an ellipticaltrainer. In other models, e.g., for other types of equipment orexercises, the model may include a variable for step rate, stroke rate,etc. “Load” represents the amount of resistance that the ellipticaltrainer (or other exercise equipment) is configured to provide toincrease the difficulty of the workout. For example, in the case of atreadmill, estimated load may represent an angle of incline (or grade orgradient) of the treadmill. As the incline of the treadmill increases,the user may need to expend more energy to maintain the same speed.

In some embodiments, at the beginning of a workout session (e.g., anelliptical trainer exercise session), the initial load may be set to adefault value. As explained below, during the workout, e.g., during an“acquisition” phase, the default load value may be adjusted to anestimated load value based on a combination of a measured work rate(e.g., step rate) and a measured heart rate.

In some embodiments, heart rate measurements may not be available forall time periods during a workout. For example, the fitness trackingdevice 100 may reduce the frequency for measuring heart rate to conservebattery power, or a particular attempt to measure heart rate may producea noisy result with a low confidence (e.g., high variance). In someembodiments, if a relatively reliable (high confidence) heart ratemeasurement is available, it may be used to update the estimated loadfor a given time period or for a subsequent time period. However, if noheart rate measurement is available, or if only a relatively unreliable(low confidence) heart rate measurement is available, the previouslyestimated load (or default load) may be used to estimate energyexpenditure for the given time period or for a subsequent time period.

In some embodiments, heart rate measurements may only be madeopportunistically, during opportunities for more accurate acquisition ofan estimated load. For example, if a user's step count is changing, itmay be an indication that a user is accelerating, e.g., at the beginningof a workout or interval, in response to adjusting a resistance of theequipment, or at the end of a workout or interval. In some embodiments,load estimation may be more accurate during a time period in which theuser's step count is relatively steady, because a steady step count maybe an indication that the user has set a pace for a given resistancelevel, which in turn may allow for more accurate estimation of the givenresistance level.

FIG. 12 shows a method for automatic load estimation based on anembodiment of the present disclosure. As show in FIG. 12, a heart rate(“HR”) may be provided by, e.g., a heart rate sensor of the fitnesstracking device 100 at block 1210 (“Heart Rate Path Optimization”). Theheart rate provides a first indication of how hard a user is working.For example, as load increases, the user's heart rate may also increase.

Additionally, as shown in FIG. 12, a step rate (“Steps”) may be providedby, e.g., a pedometer function of the fitness tracking device 100 atblock 1220 (“Step Counter”). The step rate, which is an indication ofthe user's work rate, provides a second indication of how hard a user isworking. For example, as load increases, the user may need to expendadditional energy to maintain a steady step rate (or work rate).

At block 1230 (“Load Estimation Model”), the heart rate may be receivedfrom block 1210, and the step rate may be received from block 1220. Asexplained in detail below with reference to FIG. 13, a model forestimating load may be applied at block 1230. The estimated load may beoutput from block 1230.

At block 1240 (“Calorimetry Model Feature Computation”), the loadestimate may be received from block 1230, and the step rate may bereceived from block 1240. For example, the step rate may be used todetermine the “counts/min”) input for Equation 4A for computingelliptical trainer energy expenditure. At block 1240, features relatedto the calorimetry model may be computed based on the estimated load andstep rate. In the example of the elliptical trainer, a counts/minfeature may be outputted from block 1240, and a feature representing theproduct of the counts/min and the estimated load may also be outputtedfrom block 1240.

At block 1250 (“Calorimetry Model”), one or more features may bereceived from block 1240. In the example of the elliptical trainer,features representing both the counts/min and the counts/min timesestimated load may be received at block 1240. These features may beapplied to a calorimetry model (e.g., an individually parameterizedmodel indicated by Equation 16A) to estimate energy expenditure (2260).

FIG. 13 shows additional details of an automatic load estimation method,such as the automatic load estimation method described above withreference to FIG. 12. As shown in FIG. 13, several sensors from thefitness tracking device 100 or the companion device 300 may providemotion data or other information to improve the accuracy of the loadestimation method.

For example, accelerometer data may be provided by an accelerometer atblock 1310. In some embodiments, the pedometer functionality such as thestep counter for determining step rate may be implemented or otherwisedetermined from accelerometer motion data.

In addition to accelerometer data, GPS data may be provided by a GPSsensor (block 1320) and relative altitude information may be provided byan altimeter (block 1330).

At block 1340, an activity context may be provided, which may indicatethat the user is in, for example, an elliptical trainer exercisesession. In some embodiments, the activity context may be inferred orotherwise determined by an activity classifier function based on motiondata or other information. In some embodiments, the activity context maybe determined based on user input. For example, the user may indicate tothe fitness tracking device that the user is about to begin anelliptical training exercise session.

At block 1350, a heart rate sensor, such as a PPG sensor, of the fitnesstracking device 100 may be provide heart rate information to a heartrate controller at block 1360.

At block 1360, the heart rate controller may also receive activitycontext information from block 1340. For example, if the activitycontext indicates that the user indicated to the device that the user is“in session,” the heart rate controller may request heart rateinformation relatively frequently (e.g., once per minute). However, insome embodiments, if the activity context indicates that the user'sin-session status has been inferred based on motion data, the heart ratecontroller may sample heart rate information relatively less frequently(e.g., once per three minutes) to conserve battery power.

At block 1370, a heart-rate based calorimetry model may be applied usingthe heart rate from block 1360 as an input and providing a heart-ratebased intensity (e.g., estimated {dot over (V)}O₂) to block 1380.

At block 1380, an estimated heart-rate based intensity may be receivedfrom block 1370, and work rate information based on motion or activitydata may be received from at least one of blocks 1310, 1320, 1330, and1340. The load estimator may apply, e.g., a regression analysis or adecision tree analysis to the heart-rate based intensity and step rateinputs (e.g., a “load estimation model” as described above withreference to FIG. 12 at block 1230). For example, a relatively highheart rate for a given step rate may indicate a relatively high load.The estimated load for a given time period may be output from block1380.

In some embodiments, the load estimation model may account for theactivity context. For example, in some activities, load or resistancemay be a scaling factor for another variable such as speed. Also, insome embodiments, the load estimation model may account for whetherother factors may be estimated through other techniques, or whether theymay be accounted for by the load estimate. For example, if a user isrunning with access to altimeter data (e.g., from the companion device300), the load may be estimated separately from the grade factor.However, if altimeter data is not available, the load estimate may bedetermined to account for variations in energy expenditure due to gradeas well.

In some embodiments, load filtering may be applied at block 1390. Theestimated load for the given time period may be received from block1380. The load may be filtered using historical estimated load values(e.g., hysteresis) to smooth load estimation. For example, a load filtermay limit the amount by which an estimated load may change from one timeperiod to the next. The filtered load estimate (2395) may be out output.

In some embodiments, this technique may also be applied to variousoutdoor activities (e.g., running, walking, cycling). In theseactivities, estimated load may be used to estimate other variables inthe models for these activities that affect the amount of effort that isneeded to, e.g., run or pedal a bicycle. For example, the width of thetires on a bicycle, the selected gear of the bicycle, the type of theterrain, grade of the terrain, and wind speed are some of the factorsthat may affect the total load or resistance for these activities.

For example, energy expenditure based on speed may be applied toactivities such running and cycling:Running/Cycling Intensity=(a)(speed)+(b)(speed)(grade)+(c)  (Eq. 4B)(See also Eq. 6A, below)

In Equation 4B, (a)-(c) are parameters that may be calibrated on anindividualized basis, and grade may be approximated using estimatedload.

Calorimetry for Intermittent Activity

High-intensity, short-duration activities (e.g., cross-fit training,interval running, etc.) are becoming increasingly popular techniques fortraining goals such as cardiorespiratory fitness, strength, weight loss,etc. These activities are often “untyped,” or at least complex to type,which may cause it to be more difficult to classify these activitiesautomatically. For example, interval running may appear to be similar torunning during onset of a high heart rate.

Unlike a more conventional running activity—during which the user mayhave a brief onset period, followed by an extended amount of time spentrunning with an elevated heart rate, and concluded by a cool-downperiod—interval running has relatively short periods of time spentrunning with an elevated heart rate, punctuated with frequenttransitions between periods of onset and cool-down. Thus, by combiningcalorimetry processes that may be configured to be accurate for onsetdetection, active hart-rate calorimetry, and passive cool-downcalorimetry, the fitness tracking device 100 may be able to provide amore accurate total energy expenditure estimate during these types ofintermittent training activities.

FIG. 14 shows a calorimetry method for intermittent activity inaccordance with an embodiment of the present disclosure. At block 1410,an activity context may be determined. For example, in some embodiments,the fitness tracking device 100 may receive an indication from the userthat the user is going to begin a session of intermittent activity(e.g., cross-training, interval running, yoga, weight lifting, etc.).

In parallel, at block 1420, motion data from an accelerometer, e.g.,motion data form motion sensing module 220, may also be provided toblock 1425.

At block 1425, a determination may be made as to whether the fitnesstracking device is confident that the activity context is anintermittent activity based on the motion data from block 1420 and anactivity context from block 1410. A confidence level may be output toblock 1465.

At block 1430, an instantaneous heart rate may be obtained from a heartrate sensor, e.g., heart rate data from heart rate sensing module 210,may be provided to blocks 1440 and 1450.

At block 1440, a heart rate dynamics analysis may be performed. Forexample, a first (smoothed) derivative and a second (smoothed)derivative of a sequence of heart rate samples may be computed. Thederivatives may be analyzed to predict whether the user is experiencingonset or cool-down.

For example, if the first derivative is positive (i.e., heart rate isincreasing) and the second derivative is positive (i.e., heart rate isnot only increasing; the rate at which heart rate is increasing is alsoincreasing), there may be a relatively high confidence that the user isexperience onset.

For another example, if the first derivative is negative (i.e., heartrate is decreasing) and the second derivative is zero (i.e., the rate atwhich heart rate is decreasing is constant) or the second derivative isnegative (i.e., the rate at which heart rate is decreasing is alsodecreasing), there may be a relatively high confidence that the user isexperiencing cool-down.

Additionally, heart rate data from block 1430 may also be used tocompute a current normalized heart rate (NHR) or fraction of heart ratereserve (FHR). At block 1450, a determination may be made as to whetherFHR exceeds a threshold level of FHR as a clue for onset or cool-downdetection. For example, if FHR exceeds the threshold, it may be morelikely that the user is experiencing onset of intermitting activity,whereas if FHR is less than the threshold, it may be more likely thatthe user is experience cool-down.

At block 1460, an indication of “current state” (e.g., the most recentdetermination of the user being in an onset state or cool-down state)may be fed back into block 1465 so that the calorimetry process mayaccount for onset/cool-down hysteresis.

At block 1465, a determination may be made as to whether the user iscurrently experiencing onset or cool-down. The determination process atblock 1465 may account for clues from a variety of sources, such asactivity context and confidence in activity context received from block1425, heart rate dynamics clues received from block 1440, an indicationof whether FHR exceeds a threshold value from block 1450, and feedbackabout the previously determined current state from block 1460.

The determination of onset or cool-down state at block 1465 may be usedto select an appropriate calorimetry model. For example, if it isdetermined at block 1465 that the user's (new) current state is onset,FHR may be used as a feature input to a heterogeneous, generic,high-intensity calorimetry solution for estimating calories burnedduring the onset condition at block 1470.

For another example, if it is determined at block 1465 that the user's(new) current state is cool-down, a custom time domain decay equationmay be used to model “passive” calorie burn during cool-down at block1480. In some embodiments, the cool-down calorimetry model at 1480 mayuse a custom time domain decay equation, which may be parameterizedbased on the active calorie intensity at the beginning of a cool-downphase, the elapsed duration of the current cool-down phase, and the FHR.

At block 1495, and accumulator may be used to add current caloriesburned (block 1490) to the accumulated total (block 1496).

In some embodiments, the high-intensity heart rate-based calorimetrymodel selected for onset conditions may be similar or the same as theheart rate-based calorimetry model selected for “non-intermittent”activities (e.g., running, cycling) and may be computed based at leastin part on the user's heart rate or FHR.

In some embodiments, the cool-down calorimetry model may be configuredto compute Excess Post-Exercise Oxygen Consumption (EPOC). After anindividual finishes exercising, or when an individual is temporarily atrest during an intermittent exercise such as cross-training or weightlifting, the individual may enter a cool-down period during which oxygenconsumption (and heart rate) are elevated. For example, if two users aresitting in chair at rest, but the first user has just sit down afterrunning for an hour, the first user will temporarily consume more oxygen(and expend more energy) than the second user who has been sitting forthe past hour. EPOC is the “excess” (extra) oxygen consumption that anindividual consumes during a cool-down period after exercising.

A calorimetry model that accounts for EPOC may be more accurate becauseit may be able to distinguish between the two sitting users in theexample above, the first of whom was experiencing EPOC during cool-down.

The total EPOC may be attributable to: 1) short-term replenishment ofquick-discharge power reserves (e.g., adenosine triphosphate, or ATP),2) long-term replenishment of run-of-the-mill power reserves (e.g.,glycols), 3) anaerobic expenditure-created oxygen deficits, and 4)physiological conditions during cool-down (e.g., higher body coretemperature, which may indicate a higher metabolic rate). EPOC mayinclude both a relatively fast decaying component and a relativelyslowly decaying component.

Breaking EPOC down into its component parts and modeling each part mayallow for more accurate calorimetry, particularly during intermittentexercises when individuals may switch frequently between periods ofonset and cool-down.

The fast decaying component may be based on a fixed decay rate based onthe user's physical characteristics (e.g., age, body mass index or BMI,and sex) and the user's fitness boundaries (e.g., a PAL score). In someembodiments, the fast decay function may be modeled as:{dot over (V)}O₂(t)={dot over (V)}O₂(0)·2^(−t/30)  (Eq. 5A)

According to Equation 17, the user's oxygen consumption at time t, whichmay be used to estimate energy expenditure at time t, is based on theuser's oxygen consumption at time t=0 when the current cool-down phasebegan, adjusted by the fast decay factor 2^(−t/30).

The slow decaying component may be based on a decay rate on the order ofseveral hours, which depends on fitness level (e.g., aerobic capacity,PAL score, etc.), as well as activity intensity and duration. In someembodiments, the slow decay function may be modeled as:{dot over (V)}O₂(t)={dot over (V)}O₂(0)·2^(−t/(100·{dot over (V)}O) ₂^(max))  (Eq. 5B)

According to Equation 18, the user's oxygen consumption at time t, whichmay be used to estimate energy expenditure at time t, is based on theuser's oxygen consumption at time t=0 when the current cool-down phasebegan, adjusted by the slow decay rate that includes {dot over (V)}O₂maxas part of its calculation.

The total EPOC may be attributable to: 1) short-term replenishment ofquick-discharge power reserves (e.g., adenosine triphosphate, or ATP),2) long-term replenishment of run-of-the-mill power reserves (e.g.,glycols), 3) anaerobic expenditure-created oxygen deficits, and 4)physiological conditions during cool-down (e.g., higher body coretemperature, which may indicate a higher metabolic rate). Takentogether, the total EPOC (i.e., volume of oxygen consumed), which may beused to determine calories burned during cool-down, may be modeled as:{dot over (V)}O₂(t)=(α₁){dot over (V)}O₂(0)·2^(−t/30)+(α₂){dot over(V)}O₂(0)·2^(−t/(100·{dot over (V)}O) ₂ ^(max))+(α₂)FHR+α₄  (Eq. 5C)

According to Equation 19, total oxygen consumption at time t during acool-down phase may be given as the weighted sum of the four maincomponents of EPOC. The parameters α₁-α₄ may be assigned default valuesbased on a training set. In some embodiments, a different set ofparameters based on different corresponding training sets may be useddepending on the determined activity context. For example, one set ofparameters may be used for a cross-training activity context, and adifferent set of parameters may be used for an interval running activitycontext. In some embodiments, the parameters may be calibrated orotherwise adjusted according to an individual's physical characteristicsor fitness level.

Calorimetry—Pedometry-Based Calorimetry

For low-intensity activities (e.g., walking), a pedometry-basedcalorimetry process may be more accurate than the heart rate and workrate-based calorimetry processes explained above.

As explained above with reference to FIGS. 5-10, pedometer functionalityin the fitness tracking device 100 may be used for determining timespent or distance traversed while engaged in sedentary or modest levelsof physical activity. In some embodiments, the fitness tracking device100 may be configured to estimate energy expenditure for the time spentor distance traversed during moderate levels of activity such aswalking.

In some embodiments, pedometer functionality in the fitness trackingdevice 100 may also be used for determining time spent or distancetraversed while engaged in other levels of physical activity, includingmoderate or intense levels activities. The fitness tracking device 100may be configured to estimate energy expenditure for the time spent ordistance traversed during intense levels of activity such as running.For example, if there is too much noise in a heart signal to use heartrate-based calorimetry, relatively intense activity may still beaccounted for by, e.g., pedometry functionality.

Calorimetry—Posture-Based Calorimetry

As explained above, a user will also burn calories while sedentary(e.g., sleeping, sitting, standing, or otherwise “at rest”), andcalorimetry models vary depending on the type of sedentary activity(e.g., sitting burns fewer calories than standing). Additionally,incidental motion (e.g., fidgeting) while sitting or standing burns morecalories than when there is not incidental motion.

In some embodiments, the fitness tracking device 100 may provide moreaccurate day-long calorimetry by also accounting calories burned duringsedentary activities. By performing posture detection (e.g., detectingwhether the user is sitting or standing), an appropriate calorimetrymodel may be selected according to the user's detected posture.

FIG. 15 shows a posture detection method for sedentary calorimetry inaccordance with an embodiment of the present disclosure. At blocks 1510and 1520, motion data from a motion sensor (e.g., motion data frommotion sensing module 220) may be provided to block 1530.

At block 1530, a determination of activity intensity may be made basedon the motion data received from block 1520. If the activity intensityis estimated to be moderate-to-high intensity, the fitness trackingdevice 100 may use an activity-based calorimetry model at block 1535.However, if it is determined at block 1530 that the activity intensityis low (e.g., sedentary), the method may proceed to block 1540.

At blocks 1540 and 1545, posture (e.g., sitting or standing) may bedetected. Posture detection is explained in more detail below withreference to FIGS. 16-19B. If a determination is made at block 1545 thatthe user is sitting, the method may proceed to block 1550. If adetermination is made at block 1545 that the user is standing, themethod may proceed to block 1580.

At block 1550 (i.e., user was determined to be sitting), intensity maybe estimated based on motion data from blocks 1510 and 1520. Forexample, the motion data may indicate how much the user is “fidgeting”(e.g., incidental movement, swaying, etc.), so an amount of fidgetingmay be detected at block 1550, and the amount of fidgeting may be passedto block 1560. Incidental movement or fidgeting may be relatively lowwhile sitting (e.g., typing, turning a steering wheel, etc.), andincidental movement may be relatively high while standing (e.g., armmovement while washing dishes, folding laundry, etc.).

At block 1560, a calorie model for sitting may be applied to estimatecalorie expenditure while sitting and possibly also fidgeting. Thecalories may be output at block 1570.

At block 1580 (i.e., user was determined to be standing), intensity maybe estimated based on motion data from blocks 1510 and 1520. Forexample, fidgeting (while standing) may be detected at block 1580, andan indication of whether the user is fidgeting while standing may bepassed to block 1590. Fidgeting as a generic term may include motion dueto performing low-intensity activities while standing. For example,slowly pacing, washing dishes, cooking, walking up stairs, etc. arelow-intensity activities that require more energy expenditure thanstanding still. These activities with additional movement may bedetected as relatively intense “fidgeting.”

At block 1590, a calorie model for standing may be applied to estimatecalorie expenditure while standing and possibly also fidgeting. Thecalories may be output at block 1570.

Calorie models for sitting and standing may be assigned default values(e.g., 1 MET for sitting and 2 METs for standing, or 1.5 METs forsitting and 2.5 METs for standing). Additional METs may be crediteddepending on whether the user is fidgeting and the relative intensity ofthe fidgeting. In some embodiments, the calorie models may be selectedto be higher or lower than values reported in the literature for theseactivities to reduce the amount of generalization error that may occuracross different user populations. In some embodiments, at least theinitial calorie models may be based on training data. In otherembodiments, the calorie models may be calibrated or otherwise adjustedaccording the user's physical characteristics or fitness level.

FIG. 16 depicts a posture detection method in accordance with anembodiment of the present disclosure. At block 1610, a stream of motiondata (e.g., accelerometer data) may be provided to block 1620.

At block 1620, samples of motion data from the stream of motion data maybe taken at a suitable resolution (e.g., 200 samples). 200 samples maybe taken at a frequency of 100 Hz over a time period of 2 seconds. Inother embodiments, other sample rates may be used (e.g., 256 samples or1,000 samples), or other sample frequencies may be used (e.g., 50 Hz, or200 Hz). Copies of the samples may be passed to blocks 1630 and 1650.For example, a longer duration epoch of 10 seconds may be taken tocollect 1,000 samples. However, longer durations may be less accuratebecause multiple posture changes could occur within a single epoch(e.g., a longer ten-second epoch). Similarly, shorter epochs may be lessaccurate because they may not provide a sufficient number of samples todetect posture accurately.

At block 1630, low-pass filtering (LPF) may be performed to subtract outfrequencies attributable to fidgeting or other incidental motion. Theremaining information (i.e., the portion of motion data that tends tochange slowly) may reflect the orientation of the fitness trackingdevice 100 (e.g., the angle of the fitness tracking device 100 withrespect to the horizon, explained in detail below). For example, if auser is standing with arms at the user's side, the user might befidgeting, but the angle of the fitness tracking device on the user'sarm (e.g., approximately −π/2 radians) may only vary slightly during ashort time period (e.g., 2-3 seconds). This angle information may berepresented in relatively low frequencies (e.g., less than 0.5 Hz, orless than 1.0 Hz), and this low frequency signature may be passed toblock 1640.

At block 1640, the relatively low frequency signature from block 1630may be used to compute the angle of the fitness tracking device 100 withrespect to a horizon plane (e.g., the X-Y plane). The computed angle(e.g., approximately −π/2 radians) may be passed to block 1670 as one ofthe inputs to be considered with a decision tree at block 1670.

At block 1650, band-pass filtering may be performed to obtain humanmotion information contained in a relatively higher frequency band(e.g., over 0.5 Hz, or over 1.0 Hz, and up to 5.0 Hz, or 4.0 Hz), suchas fidgeting or other incidental motion. This band's frequency signaturemay be passed to block 1660. The band-pass filter may be configured witha frequency band (e.g., 1.0-5.0 Hz) tuned to capture human motion likelyto occur when the user is sedentary (e.g., fidgeting or incidentalmotion). In some embodiments, if it may be determined that more rapid(e.g., higher frequency) human motion is likely to occur whilesedentary, the frequency band of the band-pass filter may be calibratedor otherwise adjusted accordingly.

At block 1660, the band's frequency signature from block 1650 may beused to predict posture based on the motion data received from block1650. In some embodiments, the prediction may be based on the assumptionthat the range of motion (e.g., range of wrist motion, such as when auser's arms are swaying) while standing is likely to be greater than therange of motion (e.g., wrist motion) while the user is sitting. Therange of motion may be estimated based on the motion data received fromblock 1650, which may have been filtered using a band-pass filter toinclude motion that may likely be attributable to human motion (e.g.,fidgeting).

In some embodiments, the relative range of motion during a given timeperiod (or “epoch”) may be represented as a range of amplitudes ofaccelerometer values. For example, the interquartile range (IQR) betweenthe 75th percentile and 25th percentile accelerometer amplitudes over anumber of samples (e.g., 250 samples) during the time period may beconsidered for the range of motion. For example, it some embodiments, atypical separation in ranges of motions for IQR while sitting as opposedto IQR while standing may be determined to be greater than or equal toapproximately 0.1 to 0.2 meters.

Furthermore, it may be determined that a particular axis or combinationof axes of a three-axis accelerometer within the fitness tracking device100 provides the most reliable IQR to distinguish between motion likelyoccurring while standing as opposed to motion likely occurring whilesitting. In some embodiments, for example, the x-axis of theaccelerometer may be determined to provide the most reliable IQR. Inother embodiments, a default axis or weighted combination of axes may beselected, and the selected axis or axes may be calibrated or otherwiseadjusted based on individual use. For example, the typical incidentalmotion for a user relative to the typical position and orientation ofthe fitness tracking device 100 on the user's wrist or other part of thebody may affect which axis or combination of axes may provide the mostuseful range of motion data for the IQR motion feature. This IQR motionfeature may be passed to block 1670 as a second input to be consideredwith the decision tree at block 1670.

At block 1670, the computed angle feature and the computed IQR motionfeature may be considered in the decision tree (e.g., a sequence of“if-else” conditional branches using a model with thresholds for angleand motion values). Once a posture decision has been made (e.g., theuser is sitting or the user is standing), the posture may be output atblock 1680. For example:

If (Angle < −0.5 radians and IQR > 0.1)  Return “Standing” Else If (−0.5radians < Angle < 0 radians and IQR > 0.2)  Return “Standing” Else If...

In the example decision tree above, the first condition (Angle<−0.5radians and IQR>0.1) may represent typical parameters when a user'shands are well below a horizon, though not necessarily vertically at theuser's sides. Because the angle may be sufficiently unambiguous, it maybe less important to observe a relatively large IQR to estimate that theuser is probably standing. In the second condition (−0.5 radians<Angle<0radians and IQR>0.2), the angle may be considered more shallow, and moreambiguous (e.g., the user's arms are crossed). In this situation, arelatively ambiguous angle may make it relatively more important toobserve a relatively large IQR to estimate that the user is probablystanding.

Other conditions, or additional portions of the conditions listed above,may be included in other embodiments. For example, another condition(not shown above) may indicate that if the angle is a positive value, itmay be predicted that the user is likely sitting. Alternatively, somepositive angles may be considered ambiguous. Another feature forresolving ambiguous angle and IQR features may include a pedometryfeature. For example, if a pedometer function of the fitness trackingdevice 100 determines that the user may be walking (e.g., pacing), thefitness tracking device 100 may conclude that the user had beenstanding.

In some embodiments, another feature to consider may be the frequency orrapidity of incidental movement (as opposed to the IQR feature describedabove, which indicates the amplitude or range of incidental movement).Frequency of movement may be determined by observing the number of zerocrossings (or variations around the mean) of a value of one or more axesof an accelerometer.

In some embodiments, other features in addition to, or instead of, angleand IQR motion may be considered to detect posture, such as differencesbetween X, Y, and Z accelerometer channels; mean values, vectormagnitude, activity counts (e.g., how many times a signal crosses asit/stand threshold in the epoch window), spectral power, etc. may beconsidered when detecting the user's posture. For example, in someembodiments, it may be determined that angle and IQR may be lesseffective than other features for detecting whether a user is sitting orstanding with arms crossed. Specifically, standing with arms crossed mayrestrict motion (e.g., wrist motion) relatively more than when standingwithout arms crossed. Thus, the IQR feature may be relatively low if auser is standing with arms crossed.

For this case of crossed arms, an activity count may be more likely topredict whether a user is sitting or standing. In some embodiments, thecounted activity may be zero crossings over the angle threshold (e.g.,angle crossings over the horizon). For example, it may be determinedthat the angle feature is more likely to cross a threshold angle morefrequently when the user is standing as opposed to when the user issitting, in which case a higher activity count (measured by thresholdangle crossings) may be a more accurate predictor of posture in thissituation.

In some embodiments, other classifiers in addition to, or instead of,the decision tree may be used to detect posture based on the one or moreinput features (e.g., the angle and IQR motion features). For example,random forests, a separate sit detector, a separate stand detector,support vector machines, etc., may be used to classify or otherwisedetect the user's posture.

In some embodiments, a feedback or hysteresis mechanism may be used tosmooth out possible noise in the detection output. For example, themethod may track the previous four epoch states (or more or fewer epochstates) and consider a confidence level or other indicator of which ofthe current or prior epoch states may be determined to be the dominantor most confident indicator of posture.

In some embodiments, the classifier (e.g., the decision tree used atblock 1670) may be biased toward detecting a sitting posture morefrequently. For example, ambiguous states may be more likely to resolvedas a sitting posture instead of a standing posture. In this situation,there may be fewer false positives for a standing posture, which makesit less likely that users who are sitting will not receive additionalcredit for extra energy expenditure for standing while they weresitting. In other embodiments, the decision tree may be biased tobreak-ties in favor of standing, which may make it less likely that auser who is standing may be docked credit for a false positive sitdetection.

FIG. 17 shows a posture detection method for sedentary activity inaccordance with an embodiment of the present disclosure. In someembodiments, the posture detection techniques may account for additionaldata from the companion device 300. As shown in FIG. 17, accelerometerdata may be received at block 1710 (from, e.g., motion sensing module220 of the fitness tracking device 100), and accelerometer data may bereceived at block 1720 (from, e.g., the companion device 300, when itmay be determined that the companion device is in the user's pocket).Accelerometer data from both the fitness tracking device 100 (e.g., fromblock 1710) and the companion device 300 (e.g., from block 1720) may bepassed to block 1740.

At block 1740, the user's posture may be detected. In some embodiments,the posture detection method may be similar to the method described withreference to FIG. 16. The filters and the classifier (e.g., decisiontree) may also take into account an angle feature and an IQR feature orother features from the companion device 300. The decision tree mayinclude different threshold values, weightings, and confidence levelsfor the companion device 300. For example, if the companion device isdetected to be in the user's pocket and oriented vertically, there is astrong indication that the user is standing, even if the user's arms arecrossed into a relatively ambiguous sit/stand position with respect tothe fitness tracking device 100. As another example, the fitness trackdevice 100, which may be worn on the wrist, may, at least for someactivities, receive more indications of fidgeting or other incidentalmovement than a companion device stored in a pocket near the user's hip.The detected posture (e.g., sit or stand) may be passed to blocks 1750and 1780.

At block 1750, a calorimetry process may compute energy expendedaccording to a calorimetry model based on the posture detected at block1740. The calorimetry process may also account for calories burned dueto incidental motion (e.g., fidgeting), which may be detected using theIQR motion feature passed from block 1710 through a band-pass filter.The sedentary calorie count (“SedCal”) may be outputted at block 1760.

FIG. 17 also includes a timer feature for tracking how much time isspent in sedentary postures, such as with limited incidental motionbelow a predetermined threshold. In some embodiments, a step count maybe determined at block 1770 (e.g., using pedometer functionality of thefitness tracking device 100). The step count may be passed to block1780.

In some embodiments, at block 1780, a timer may track whether the useris sedentary, and for how long. It may account for detected posturereceived from block 1740, IQR motion feature received from block 1710,step count received from block 1770, etc. The amount of sedentary time(“SedTimer”) may be output at block 1790. In some embodiments, if thesedentary timer detects that a user has been sitting or otherwisesedentary for a prolonged period of time, the fitness tracking device100 may be configured to alert the user to encourage the user to standup or otherwise move around.

FIG. 18 illustrates a posture detection technique in accordance with anembodiment of the present disclosure. As shown in FIG. 18, horizontalline (1810) represents the horizon 1810 (e.g., the X-Y horizon plane). Afirst arc 1820 represents angles above the horizon 1810. A second arc1730 represents angles below the horizon 1810. In the example of FIG.18, a threshold angle may be set at 0 radians (i.e., in-line with thehorizon 1810). If the detected angle feature is an angle within thefirst arc 1820, the classifier (e.g., decision tree) may consider it astrong clue that the user has a first posture (e.g., sitting).Similarly, if the detected angle feature is an angle within the secondarc 1730, the classifier may determine that the user has a secondposture (e.g., standing).

In some embodiments, the orientation of the fitness tracking devicerelative to the horizon plane (e.g., horizon 1810) may be determinedbased on motion data received from a three-axis accelerometer, athree-axis gyroscope, or a combination of a three-axis accelerometer anda three-axis gyroscope, such as the motion sensors that may be includedin motion sensing module 220 of the fitness tracking device 100.

In other embodiments, a different threshold angle may be selected toindicate which range of angles are more likely to be assumed while in asitting posture, and which range of angles are more likely to be assumedwhile in a standing posture. In some embodiments, the ranges mayoverlap, and the overlapping regions are angles for which the posturemay be ambiguous.

FIGS. 19A-B illustrate posture detection techniques in accordance withembodiments of the present disclosure. As shown in FIG. 19A, a firstuser 1910 is standing with arms at the user's side. The first user 1910is wearing the fitness tracking device 100 on the user's wrist, and itis oriented at an angle 1915 that is below the horizon 1810. In thisexample, a posture detection algorithm may evaluate the angle 1915 anddetermine correctly that the first user 1910 is standing.

As shown in FIG. 19B, a second user 1920 is sitting with arms extendedout, typing on a keyboard while sitting at a desk. The second user 1920is also wearing the fitness tracking device 100 on the user's wrist, andit is oriented at an angle 1925 that is approximately at the horizon1810. In this example, a posture detection algorithm may consider thisangle to be ambiguous. For example, the angle may be insufficient todistinguish between a user sitting and typing or standing with armscrossed at an angle roughly level with the horizon.

In this case, it may be possible to resolve the ambiguity based on IQRmotion. If the user is sitting, as the second user 1920 is sitting, itmay be more likely that IQR motion will be relatively low as compared tothe IQR motion of a user standing with arms in as similar orientation.

Additionally, the second user 1920 is depicted as holding the companiondevice 300 in a pants pocket. In some embodiments, it may be detectedthat the companion device 300 is in the pocket and orientedhorizontally, in-line with the horizon 1810. This orientation of thecompanion device 300 may be a strong indicator that the second user 1920is in a sitting posture.

Calorimetry—Additional Optimizations for Resource-Constrained Devices

The fitness tracking device 100 may be a small device with limited spacefor memory and a battery, and it may be designed to be worn all daywhile running on battery power. Consequently, some embodiments mayinclude optimizations to perform functions within relatively limitedresources as compared to the companion device 300 or another computingdevice. For example, the fitness tracking device 100 may use a low-powermotion coprocessor to monitor motion data with less power usage than ifthe primary microprocessor were continuously monitoring the motion data.As another example, the fitness tracking device 100 may reduce thefrequency at which it monitors heart rate data to conserve power untilthe fitness tracking device 100 detects or otherwise determines that theuser is engaged in an activity for which the calorimetry process wouldbenefit from more frequent heart rate monitoring.

To illustrate the benefits of the local model for at least certaincircumstances, FIG. 20 shows a speed-based calorimetry method inaccordance with an embodiment of the present disclosure.

In some embodiments, the fitness tracking device 100 may be configuredto predict energy expenditure using a comparably accurate but lessresource-intensive calorimetry process dubbed the “local model.” Atblock 2010, accelerometer or other motion data 2015 may be passed toblocks 2020 and 2030.

At block 2020, speed may be estimated based on the motion data receivedfrom block 2010. In some embodiments, the accuracy of the speed estimate2025 may be improved with GPS location data (e.g., GPS location datareceived from the companion device 300). The speed estimate may beprovided to block 2040

At block 2030, other features may be determined from the motion data orother inputs. These other features may also be provided to block 2040.

At block 2040, a calorimetry model may be used to estimate energyexpenditure based on estimated speed 2025 from block 2020 and otherinput features from block 2030. The calorimetry model may be selected tobalance accuracy with complexity (whereby complexity may place increasedstrains on device resources, such as processing power, memory, batterylife, etc.)

For example, the American College of Sports Medicine (ACSM) has a(nonlinear) model for energy expenditure while walking:{dot over (V)}O₂=0.1(speed)+1.8(speed)(fractional grade)+3.5  (Eq. 6A)

Equation 6A is a nonlinear model relying on speed and, optionally,grade, as the only inputs. The parameterization is offered as aone-size-fits-all approach. This calorimetry model has minimalcomplexity, but it may also have low accuracy, particularly for userswho are “average” for whom the given parameters may not be a good fit.

At the other end of the spectrum may be highly complex (and/ornonlinear) techniques such as context-aware machine learning models(e.g., neural networks, regression trees, random forests, etc.). Arandom forest, for example, may be a highly accurate but also can be ahighly complex nonlinear model, which may require more time andcomputing resources (including memory and batter power).

The local model may offer high accuracy that is comparable to a randomforest, but the complexity (and therefore the resource requirements) maybe substantially lower than those of the random forest.

FIG. 21 shows a local model-based calorimetry method in accordance withan embodiment of the present disclosure. Unlike the ACSM'sone-size-fits-all model, the local model may still account fordifferences among users based on age, sex, body mass index (BMI), etc.Instead of building a customized model for every permutation of age,sex, and BMI, each variable may be grouped into a suitable number ofbins (or groups). For example, age may be split into bins such as 20-29,30-39, 40-49, 50-59, etc., and BMI can be split into bins such as16-19.9, 20-23.9, 24-27.9, 28-31.9, etc. In other embodiments, fewerbins or more bins may be used for age or BMI, and different ranges maybe selected.

As shown in FIG. 21, physical characteristics (“anthropomorphicvariables”) may be collected. These characteristics may include sex(e.g., male or female, sometimes referred to as “gender”), age (e.g.,0-120), and BMI (e.g., 16-32). In some embodiments, the fitness trackingdevice 100 may receive the user's height and weight to determine theuser's BMI. In other embodiments, more or fewer characteristics orvariables may be taken into account.

At block 2120, sex (“gender”) may be mapped to a “sex bin index” (or“gender bin index”) (e.g., female=index 0, male=index 1).

At block 2130, age may be mapped to an “age bin index.” For example,using the age bins described above, if the user's age is given as 42,the user's age falls into the “40-49” bin. This bin may be designated asage bin index number 2.

At block 2140, BMI may be mapped to a “BMI bin index.” For example,using the BMI bins described above, if the user's BMI is given as 21.2,the user's BMI falls into the “20-23.9” bin. This bin may be designatedas bin index number 1.

In other embodiments, other variables may be similarly mapped as thesex, age, or BMI mappings described above.

At block 2150, a calorimetry model may be selected based on the sex binindex number received from block 2120, the age bin index number receivedfrom block 2130, and the BMI bin index number received from block 2140.For example, in some embodiments, a calorimetry model trained,calibrated, or otherwise adjusted for a particular permutation of sex, asub-range (bin) of ages, and a sub-range (bin) of BMI values. Becauseeach variable may be indexed to a relatively small number of bins, onlya relatively small number of calorimetry models need to be stored orgenerated. In some embodiments, the sub-ranges for each bin and the sizeof each bin may be configured to provide an appropriate balance betweenaccuracy and complexity.

In one example, the ACSM model may be generalized:VO₂=α₁(speed)+α₂(speed)(fractional grade)+α₃  (Eq. 6B)

In the general form, the (nonlinear) ACSM model has three coefficientsα₁-α₃. The local model technique may store the three coefficientssuitable for each permutation of bins. Thus, in an example for whichthere are 2 sex bins, 4 age bins, and 4 BMI bins, this configuration maystore coefficients for 2×4×4=32 permutations of bins.

The memory required to store 32 triplets of coefficients may be modestcompared to a more complex technique and may not be meaningfully largerthan storing only single one-size-fits-all model. For example, for aconventional decision tree, the computational complexity may beproportional to the depth of the tree d, which may be generally between10 and 20. Because the storage required may be proportional to 2^(d),which is on the order of one thousand to one million sets ofcoefficients. For another example, a random forest may typically requireone hundred times more storage than a decision tree. In someembodiments, in which a linear model is used, only 32 pairs or doubletsof coefficients may need to be stored. For higher-order nonlinearmodels, additional parameters may need to be stored for eachpermutation.

Also, the model selection technique of sorting variables intopredetermined bins, and using the bin index numbers to look up a set ofcoefficients in a relatively small lookup table may require much lessbattery power and processing power than a more complex technique such asa random forest. Furthermore, once a model is selected, it may requireno more battery power, processing power, or memory than aone-size-fits-all model.

At block 2170, an energy expenditure prediction process may use themodel 2155 received from block 2150 to estimate energy expenditure giventhe speed and other inputs provided from block 2160.

At block 2180, the estimated energy expenditure may be outputted.

CONCLUSION

The present disclosure is not to be limited in scope by the specificembodiments described herein. Indeed, other various embodiments of andmodifications to the present disclosure, in addition to those describedherein, will be apparent to those of ordinary skill in the art from theforegoing description and accompanying drawings. Thus, such otherembodiments and modifications are intended to fall within the scope ofthe present disclosure. Further, although the present disclosure hasbeen described herein in the context of at least one particularimplementation in at least one particular environment for at least oneparticular purpose, those of ordinary skill in the art will recognizethat its usefulness is not limited thereto and that the presentdisclosure may be beneficially implemented in any number of environmentsfor any number of purposes.

The invention claimed is:
 1. A method comprising: obtaining, by a heartrate sensor of a fitness tracking device, a heart rate measurement of auser of the fitness tracking device at a first sampling rate in responseto determining that the user is in a particular activity session and ata second sampling rate at other times, wherein the first sampling rateis higher than the second sampling rate; obtaining, by at least onemotion sensor, motion data of the user; analyzing, by the fitnesstracking device, the motion data of the user to estimate a step rate ofthe user; estimating, by the fitness tracking device, a load associatedwith a physical activity of the user by comparing the heart ratemeasurement with the step rate of the user; and estimating, by thefitness tracking device, an energy expenditure rate of the user using acombination of the load and: the heart rate measurement when the heartrate measurement is obtained at the first sampling rate, and the steprate when the heart rate measurement is obtained at the second samplingrate.
 2. The method of claim 1, wherein the physical activity of theuser comprises training on an elliptical training machine, and whereinthe load comprises a resistance setting of the elliptical trainingmachine.
 3. The method of claim 1, wherein the physical activity of theuser comprises running on a treadmill, and wherein the load comprises anincline setting of the treadmill.
 4. The method of claim 1, wherein thephysical activity of the user comprises stationary cycling, and whereinthe load comprises a resistance setting of a stationary cycle.
 5. Themethod of claim 1, comprising: analyzing, by the fitness trackingdevice, the motion data to determine a speed of the user, wherein thephysical activity of the user comprises running, and wherein the loadcomprises a grade of a terrain on which the user comprises running. 6.The method of claim 1, wherein a first heart rate measurement indicatesa first load for a given step rate, and wherein a second heart ratemeasurement different from the first heart rate measurement indicates asecond load for the given step rate different from the first load forthe given step rate.
 7. The method of claim 1, wherein a first step rateindicates a first load for a given heart rate, and wherein a second steprate different from the first step rate indicates a second load for thegiven heart rate different from the first load for the given step rate.8. A fitness tracking device comprising: a heart rate sensor formeasuring a heart rate of a user of the fitness tracking device at afirst sampling rate in response to determining that the user is in aparticular activity session and at a second sampling rate at othertimes, wherein the first sampling rate is higher than the secondsampling rate; a motion sensor for sensing motion of the user; and aprocessor communicatively coupled to the heart rate sensor and themotion sensor, wherein the processor is configured to: obtain a heartrate measurement of the user from the heart rate sensor; obtain motiondata of the user from the motion sensor; analyze the motion data of theuser to estimate a step rate of the user; estimate a load associatedwith a physical activity of the user by comparing the heart ratemeasurement with the step rate of the user; and estimate an energyexpenditure rate of the user using a combination of the load and: theheart rate measurement when the heart rate measurement is obtained atthe first sampling rate, and the step rate when the heart ratemeasurement is obtained at the second sampling rate.
 9. The fitnesstracking device of claim 8, wherein the physical activity of the usercomprises training on an elliptical training machine, and wherein theload comprises a resistance setting of the elliptical training machine.10. The fitness tracking device of claim 8, wherein the physicalactivity of the user comprises running on a treadmill, and wherein theload comprises an incline setting of the treadmill.
 11. The fitnesstracking device of claim 8, wherein the physical activity of the usercomprises stationary cycling, and wherein the load comprises aresistance setting of a stationary cycle.
 12. The fitness trackingdevice of claim 8, where the processor is configured to: analyze themotion data to determine a speed of the user, wherein the physicalactivity of the user comprises running, and wherein the load comprises agrade of a terrain on which the user is running.
 13. The fitnesstracking device of claim 8, wherein a first heart rate measurementindicates a first load for a given step rate, and wherein a second heartrate measurement different from the first heart rate measurementindicates a second load for the given step rate different from the firstload for the given step rate.
 14. The fitness tracking device of claim8, wherein a first step rate indicates a first load for a given heartrate, and wherein a second step rate different from the first step rateindicates a second load for the given heart rate different from thefirst load for the given step rate.
 15. An article of manufacturecomprising: a non-transitory processor readable storage medium; andinstructions stored on the medium; wherein the instructions areconfigured to be readable from the medium by a processor of a fitnesstracking device comprising a heart rate sensor and a motion sensor andthereby cause the processor to operate to: obtain a heart ratemeasurement of a user of the fitness tracking device from the heart ratesensor at a first sampling rate in response to determining that the useris in a particular activity session and at a second sampling rate atother times, wherein the first sampling rate is higher than the secondsampling rate; obtain motion data of the user of the fitness trackingdevice from the motion sensor; analyze the motion data of the user toestimate a step rate of the user; estimate a load associated with aphysical activity of the user by comparing the heart rate measurementwith the step rate of the user; and estimate an energy expenditure rateof the user using a combination of the load and: the heart ratemeasurement when the heart rate measurement is obtained at the firstsampling rate, and the step rate when the heart rate measurement isobtained at the second sampling rate.
 16. The article of manufacture ofclaim 15, wherein the physical activity of the user comprises trainingon an elliptical training machine, and wherein the load comprises aresistance setting of the elliptical training machine.
 17. The articleof manufacture of claim 15, wherein the physical activity of the usercomprises running on a treadmill, and wherein the load comprises anincline setting of the treadmill.
 18. The article of manufacture ofclaim 15, wherein the physical activity of the user comprises stationarycycling, and wherein the load comprises a resistance setting of astationary cycle.
 19. The article of manufacture of claim 15, where theprocessor is caused to: analyze the motion data to determine a speed ofthe user, wherein the physical activity of the user comprises running,and wherein the load comprises a grade of a terrain on which the user isrunning.
 20. The article of manufacture of claim 15, wherein a firstheart rate measurement indicates a first load for a given step rate, andwherein a second heart rate measurement different from the first heartrate measurement indicates a second load for the given step ratedifferent from the first load for the given step rate.